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In this episode, we're joined by David Soria Parra, Engineering Lead at Anthropic and one of the core maintainers of the Model Context Protocol (MCP), to explore the biggest evolution of the protocol since its launch, and why MCP is becoming the foundation for the next generation of AI agents.We discuss why MCP is moving toward stateless communication, what developers misunderstand about state, sessions, and transport layers, and how lessons from real-world deployments at massive scale have shaped the protocol's future. We also dive into MCP v2, SDK migrations, protocol design, extension architecture, governance, developer experience, and how Anthropic thinks about balancing simplicity with long-term flexibility.Along the way, we explore progressive disclosure, tool search, programmatic tool calling, context bloat, forward compatibility, long-running AI tasks, protocol evolution, open-source governance, observability, and why the future of AI infrastructure will depend on designing protocols that can evolve without breaking the ecosystem.Timestamps:[00:00] Introduction[01:59] Why MCP Had to Become Stateless[04:28] The Tradeoffs of Stateless Design[06:13] What We Learned About Agent State[08:04] Sessions, Models & Implicit State[09:33] Migrating to MCP v2[12:19] Lessons from HTTP & Open Source Standards[18:16] Shipping Fast Without Breaking Everything[20:35] The Future Complexity of MCP[22:44] Core Features vs Extensions[26:47] Progressive Disclosure Explained[28:16] Solving Context Bloat[30:50] Why Tool Search Beats Progressive Disclosure[32:10] The Biggest MCP Anti-Pattern[34:25] Designing for Forward Compatibility[38:41] Why "Tasks" Matter[40:53] JSON, Tokens & Better Tool Calling[44:44] Observability & Tracing AI Agents[47:34] Will MCP Ever Be Finished?[50:22] What's Next for MCP

Manish Dasaur is a Managing Director at PwC with over 20 years in data and AI, having helped 100+ clients navigate AI disruption and extract real business value from data, AI, and agentic AI initiatives. In this episode, he breaks down why most enterprise AI programs stall — and the playbook the winners are using instead.Huge thanks to PwC for supporting this episode!💰 The 30% benchmark — What "good" actually looks like: real efficiency gains clients are reporting across engineering, finance, HR, and supply chain🔄 Workflows, not use cases — Why isolated pilots and POCs never show up in EBITDA, and how end-to-end workflow redesign does🧪 Champion vs. challenger — Running a control group against your AI-automated process so ROI is demonstrated, not guessed📞 Why customer care agents are still freaking hard — Context, CDP integration, billing systems, and voice-to-voice latency💸 Tokenomics & FinOps — Consumption-based cost surprises, model selection, prompt engineering, and enforcing cost-per-workflow budgets🔍 Auditing agentic behavior — Using AI to test AI, the missing "SOC 2 for agents," and certifying agents for sensitive use cases👤 Human in the loop as an evolving scale — From reviewing 50% of outputs down to 10% as trust builds🧠 88% do AI, 33% scale it — Building a culture of innovation, and why AI usage is showing up in performance reviews💼 Jobs, reskilling & the operating model reset — Why 75%+ of jobs will be reskilled, not replacedIf you're an AI leader, platform engineer, or exec trying to turn AI experiments into P&L impact, this one's for you.Links & Resources:Connect with Manish: https://www.linkedin.com/in/manishdasaur/PwC AI: https://www.pwc.com/us/en/tech-effect/ai-analytics.htmlPwC's 2026 AI Business Predictions: https://www.pwc.com/us/en/tech-effect/ai-analytics/ai-predictions.htmlTimestamps:[00:00] AI Hype vs Business Value[00:44] API Spend Tracker Widget[02:18] Tokenomics and FinOps for AI[06:19] Measuring AI Impact Objectively[11:07] AI in Support Workflows[18:05] AI Innovation Culture[27:16] MCP Servers and SOC 2[29:14] Human in the Loop in evolving scale[35:44] AI and Workforce Efficiency[39:59] AI Transformation and Mindset[42:19] Wrap-up

In this episode, we're joined by Jeremiah Lowin, Founder & CEO at Prefect and the creator of FastMCP, to explore how one of the most influential projects in the MCP ecosystem came to be - and where the protocol is heading next.We discuss the accidental origin of FastMCP, why Anthropic adopted it into the official SDK, what developers are getting wrong about MCP, and why Chris believes the biggest opportunity for AI agents isn't customer-facing applications, but internal enterprise systems. We also dive into MCP Apps, developer experience, protocol design, AI tooling, Python, and why building great abstractions is often more valuable than exposing more configuration.Along the way, we explore the rapid growth of the MCP ecosystem, how FastMCP became the default way many developers build MCP servers, why "too much magic" can actually hurt developer experience, and what the next generation of AI-powered applications will look like as agents move beyond simple tool calling into rich, interactive experiences.Prefect: https://www.prefect.ioJeremiah Lowin: https://www.linkedin.com/in/jlowinDemetrios: https://www.linkedin.com/in/dpbrinkmTimestamps:00:00 Lost My Entire Talk00:47 The Story Behind FastMCP02:08 Anthropic Adopted FastMCP02:34 When MCP Took Off04:10 FastMCP vs The Official SDK05:43 Is MCP Actually Dead?06:42 What Everyone Gets Wrong About MCP08:11 MCP's Biggest Use Case10:25 Building Internal AI Systems12:00 Why FastMCP Exploded13:29 Making Complex Software Simple15:10 Can Software Be Too Magical?20:11 MCP Apps Explained23:42 Why Python Needed MCP Apps27:54 The Future of AI Interfaces34:18 AI Should Generate UIs40:11 AI Deleted My Presentation43:30 The AI Assistant We Actually Need48:00 Personal AI vs SaaS52:28 The Future of AI Agents55:06 Final Thoughts

In this episode, we're joined by Stephen O'Grady, Co-Founder and Principal Analyst at RedMonk, to explore one of the biggest shifts happening in software engineering: AI is making code dramatically cheaper to produce, but everything downstream is becoming the new bottleneck.We discuss why SaaS isn't dead despite the hype, the explosive rise of MCP, why AI agents are overwhelming developer infrastructure, and what happens when every engineer suddenly has dozens of AI developers working alongside them. Stephen explains how package managers, code reviews, security, governance, and enterprise systems are all struggling to keep pace with AI-generated software.Along the way, we dive into AI coding tools, MCP adoption, developer productivity, infrastructure scaling, enterprise software, open source, package repositories, governance, and why the hardest problems in software may no longer be writing code—but managing everything that comes after.RedMonk: https://redmonk.comStephen O'Grady: https://www.linkedin.com/in/sogradyDemetrios: https://www.linkedin.com/in/dpbrinkm

In this episode, we're joined by Matt DeBergalis, CTO and Co-Founder of Apollo GraphQL, to explore what happens when AI agents start interacting with enterprise systems that were never designed for them.We dive into the collision between APIs, MCP, GraphQL, and agentic AI, and why traditional assumptions about trust, permissions, and security are breaking down. Matt argues that AI agents should be treated as untrusted actors by default, and explains why giving agents access to enterprise data creates entirely new challenges around governance, access control, and risk management.Along the way, we discuss semantic APIs, enterprise data silos, citizen developers, agent permissions, security boundaries, and how GraphQL and MCP can work together to make enterprise systems more accessible to both humans and AI. The conversation also explores why companies are racing to deploy agents despite the risks, and what the future of enterprise software might look like when AI becomes the primary consumer of APIs.Apollo GraphQL: https://www.apollographql.comMatt DeBergalis: https://www.linkedin.com/in/debergalisAlex Salkever: https://www.linkedin.com/in/alexsalkeverTimestamps:[00:00] AI, APIs, and Trust[01:16] MCP API Lessons[06:16] GraphQL and MCP Integration[12:55] API Security for MCP[16:10] Linux Kernel Security Concerns[19:09] API Design and Controls[21:52] Trust in Autonomous Systems[25:06] MCP GraphQL Wish List[27:13] API Access Patterns[28:44] GraphQL API Perspective

In this episode of Agentic Conversations, we're joined by Shaun Smith, software engineer, open source advocate, and contributor at Hugging Face, to explore how AI coding has changed almost overnight.We dive into reinforcement learning, MCP (Model Context Protocol), Fast Agent, Claude Code, open source AI, and why today's language models have become so capable that many traditional software libraries are becoming "liquefied." Shaun explains how reinforcement learning unlocked long-running autonomous agents, why ideas are becoming more valuable than code, and how developers should think about building software in an era where AI can generate entire applications.Along the way, we discuss Hugging Face's MCP server, Fast Agent, AI-powered developer tools, multimodal applications, MCP Apps, context windows, coding assistants, Rust, Python, TypeScript, open-weight models, software architecture, and what the future of programming looks like when humans increasingly focus on design instead of implementation.Shaun Smith: https://www.linkedin.com/in/smithshaunDemetrios: https://www.linkedin.com/in/dpbrinkmHugging Face: https://huggingface.co⏱️ Timestamps[00:00] Introduction[01:56] The State of Open Source AI[05:18] Reinforcement Learning Changed Everything[07:50] Fast Agent Explained[10:18] Fast Agent as an MCP Reference Platform[12:20] Building Smarter AI Tools at Hugging Face[15:17] Natural Language Search Instead of APIs[17:46] Why MCP Apps Matter[20:06] The Evolution of MCP Apps[23:05] Building AI-Native User Interfaces[26:12] Context Is the New Programming Language[28:00] The End of Code Libraries[29:50] Why Developers Aren't Writing Code[31:25] AI Changes Software Engineering[33:05] The Future of Open Source AI[35:43] Claude Skills That Save Hours[38:02] Training Models with AI[39:05] Building Your Own AI Tools[40:50] MCP for Consumers, Enterprises, and Developers[43:42] Why Shell Access Makes Agents Smarter[45:18] Secure Agent Workflows[46:08] The Future of AI Interfaces[47:02] Outro#HuggingFace #MCP #OpenSourceAI

In this episode, we're joined by Ben Morss, Developer Advocate at DeepL, who spent months traveling across North America and Europe teaching developers about MCP, building MCP servers, and helping teams understand how AI agents actually use tools.We dive into the biggest misconceptions around MCP, why so many developers still misunderstand how it works, and what Ben learned after giving talks and workshops in 10 cities across four countries. Along the way, we explore MCP server design, tool calling, security concerns, translation workflows, developer education, and how DeepL is using MCP to bring high-quality language translation into AI-powered applications.DeepL: https://www.deepl.comBen Morss: https://www.linkedin.com/in/ben-morss-ph-d-15bab15/Alex Salkever: https://www.linkedin.com/in/alexsalkeverTimestamps:[00:00] AI and API Integration[00:41] DeepL at DevSummit[01:19] MCP Roadshow Origins[03:47] MCP Hackathon Insights[07:52] Security in Model Protocols[10:25] AI Expert vs Noob Queries[16:08] DeepL vs Frontier LLMs[18:16] MCP vs REST API[21:39] MCP Servers and DeepL

In this episode, we're joined by Cornelia Davis, Developer Advocate at Temporal and a longtime software architect who has spent decades helping shape modern cloud-native systems.We explore how programming has evolved from assembly language to cloud-native architectures, and why AI is forcing us to rethink software development once again. Cornelia argues that natural language is becoming a new programming abstraction, while durable execution may be the missing layer that makes AI agents reliable in production.The conversation dives into probabilistic software, long-running AI agents, MCP tasks, human-in-the-loop workflows, durable timers, distributed systems, and why developers may no longer need to think about infrastructure the way they once did.Cornelia Davis: https://www.linkedin.com/in/corneliadavisDemetrios: https://www.linkedin.com/in/dpbrinkmTemporal: https://temporal.ioTimestamps[00:00] AI Programming Abstractions[00:52] Abstraction Evolution in Programming[04:05] Text to SQL Evolution[10:08] Compensations for Natural Language[12:13] Durable MCP in AI[18:34] Streaming Session Explanation[21:31] Batch Processes with Tasks[29:29] Complexity Relocation in Systems[33:10] Complexity Relocation in Dev[36:36] Programming Model Shifts

Denny Lee is PM Director, Startups & Ecosystem at Databricks, a longtime Apache Spark, MLflow, and Delta Lake contributor — and one of the people behind Omnigent, the open-source meta-harness Databricks just released under Apache 2.0. He joins Demetrios to explain why the industry is moving from models to harnesses to meta-harnesses, why token spend is replaying the CapEx-to-OpEx shift all over again, and why he's using debating AI agents to plan a matcha farm in Taiwan.In this episode:🍵 Agents as research partners — Denny uses dueling agents to scout matcha-growing regions in Taiwan, down to soil pH, elevation, and processing infrastructure🥊 Why agents should debate each other — letting two models argue surfaces the questions you didn't know to ask🔱 Forking conversations — the missing UX pattern: branch a session, keep the shared context, explore two threads in parallel🧠 The meta-harness layer — how Omnigent sits above Claude Code, Codex, Pi, and custom agents so models and harnesses become hot-swappable parts👥 The two-pizza rule for agents — military span-of-control logic says you can manage 5–7 agents before you lose the thread💸 Tokenomics is the new DevOps — the CapEx→OpEx playbook repeats: give developers spend visibility, keep central governance for the rest🛡️ Policies, budgets, and guardrails — enforcing cost caps and approval rules at the harness layer instead of inside prompts🤖 Auto model selection — why classic machine learning (not another LLM) may be the right way to route tasks to cheap vs. frontier models✍️ "Created by" vs. "assisted by" — the open source accountability debate: whoever submits the code owns the code🗄️ Databases are back — agents need cheap, stateful memory, which is why Postgres, Lakebase, and serverless databases are having a momentIf you're building with coding agents, managing AI spend, or trying to keep up with the harness arms race, this one's for you.Links & Resources:Omnigent (open source): https://www.databricks.com/blog/introducing-omnigent-meta-harness-combine-control-and-share-your-agentsOmnigent GitHub: https://github.com/databricks/omnigentDenny Lee on LinkedIn: https://www.linkedin.com/in/dennygleeDenny's blog: https://dennyglee.comTokenomics Foundation announcement: https://www.finops.org/insights/finops-x-2026-day-1-keynote/Timestamps: [00:00] SOA to LLMOps Transition[01:06] Agentic Research Workflow[10:45] Agent Debate for Execution[13:53] Agentic Footnote Concept[24:41] Harnesses in Agent Systems[32:43] Harnessing Multi-Layered Agents[38:06] Token Spending Awareness[41:01] Token Spend Efficiency[43:53] Model Selection Frustration[51:06] Meta Harness in AI[53:15] Harness Layers Model[57:17] Wrap up#Tokenomics #AIAgents #Omnigent

Qdrant Roundtable episode: The Current State of Agentic RetrievalJoin the Community: https://go.mlops.community/YTJoinInGet the newsletter: https://go.mlops.community/YTNewsletterMLOps GPU Guide: https://go.mlops.community/gpuguideBig shout-out to Qdrant for the collaboration!// AbstractAI agents are only as good as the information they can find, retrieve, and remember. In this community roundtable with the Qdrant team, we explored the latest advances in agentic memory, vector search, retrieval systems, and production AI architectures.As AI agents move beyond simple chatbots into systems that can reason across large amounts of information, retrieval is becoming one of the most important layers in the AI stack. The discussion covered the real-world challenges of building agents that remember what matters, forget what doesn't, and consistently retrieve the right context at the right time.If you're building AI agents, RAG systems, or production AI applications, this conversation offers practical insights into where retrieval is headed and what it takes to build reliable, scalable agentic systems.// BioEwa SzyszkaEwa is a Developer Relations professional based in San Francisco with a background in Computer Science and Hardware Engineering, passionate about bridging the gap between technology and the developer community. She holds a BSc in Computer Science and an MSc in Electronics, bringing a strong blend of deep technical foundations and communication skills to her work.Dylan CouzonDylan is based in New York City, and he helps developers build better AI applications. He is passionate about AI, programming, open source, and robotics, and enjoys sharing what he’s building and learning along the way.Neil KanungoNeil is an experienced professional with expertise in data science, developer relations, and product growth. Currently serving as the Head of Developer Relations at Qdrant, Neil previously held the position of VP of Product Led Growth & Developer Relations at KX, where significant increases in product registration and user activation were achieved. At TIBCO, Neil managed a team focused on enhancing the adoption of TIBCO Spotfire through various initiatives, including tutorial videos and live webinars. With a strong technical background, Neil has developed innovative solutions in analytics, machine learning, and data visualization across multiple roles, including Engineering Data Analyst and Asset Integrity Engineer at Enterprise Products. Neil holds a Bachelor of Science in Radiation Physics from The University of Texas at Austin, a Master of Science in Mechanical Engineering from Texas Tech University, and is pursuing a Master in Applied Data Science from the University of Michigan.Evgeniya SukhodolskayaDeveloper Relations at Qdrant with 8 years of IT experience across software engineering, machine learning, and technical management, and 4 years in Developer Relations. Holds a Master’s in Machine Learning, Data Analytics, and Data Engineering. Passionate about NLP, data-centric AI, and the role of vector search in advancing AI technologies.Andrei CristeaAndrei is a Berlin-based Developer Relations Engineer at Qdrant, a prominent open-source vector database. With a Master’s degree in Artificial Intelligence from TU Munich, his expertise bridges AI, data infrastructure, and knowledge engineering.Hosted by Demetrios// Related LinksWebsite: https://qdrant.tech/~~~~~~~~ ✌️Connect With Us ✌️ ~~~~~~~Catch all episodes, blogs, newsletters, and more: https://go.mlops.community/TYExploreJoin our Slack community [https://go.mlops.community/slack]Follow us on X/Twitter [@mlopscommunity](https://x.com/mlopscommunity) or [LinkedIn](https://go.mlops.community/linkedin)] Sign up for the next meetup: [https://go.mlops.community/register]MLOps Swag/Merch: [https://shop.mlops.community/]

Kingsley Madikaegbu is the founder of HealID, a startup building agentic AI on top of the Model Context Protocol (MCP) for one of the most heavily regulated environments there is: healthcare.Recorded at MCP Dev Summit North America in New York, Kingsley sits down with Alex Salkever of the Agentic AI Foundation to break down how you give patients, doctors, caregivers, and family members each their own agent over the same medical record — without breaching HIPAA, leaking PHI, or letting an agent quietly go off the rails. In this conversation:🏗️ The four-layer architecture — Dumb data at the bottom, then access permissions, then MCP, then reasoning agents on top. Why logic never touches the data layer.🔐 MCP vs REST — Why enforcing per-role compliance in a REST API meant encoding permissions everywhere, and how MCP collapses that mess.🪪 HIPAA, auditability & traceability — Proving a specific person (not a snooping agent) accessed a record, with a full audit trail that regulators actually accept.🎟️ The nightclub-bouncer analogy — How MCP reorganizes the entire "club" per guest instead of just checking a VIP list.⌚ Wearables & real-world data — Turning an Apple Watch arrhythmia signal into a triaged, severity-scored workflow with doctors in the loop.🧭 Deterministic vs model-driven — Why anything clinical or regulatory stays binary, and the agent-as-coach (not decision-maker) pattern for patients.🛑 Keeping agents on the leash — Tool restriction, behavioral metadata, and drift/anomaly detection so an agent can't reinterpret its own job.⚡ The instant kill switch — Revoke permission, and the agent returns a hard 404, never partial data.⚖️ The liability question — When an agent follows a designed workflow and something goes wrong, who's responsible: patient, host, or provider? The industry hasn't decided.📋 Kingsley's MCP wishlist — Built-in traceability (OTEL-style spans), native time-bound enforcement, and guardrails against agent-to-agent data leakage.If you're building agentic systems for healthcare, finance, legal, or any regulated industry where "the agent did it" isn't a good enough answer — this one's for you.Links & Resources🔗 HealID — https://gethealid.com/🔗 Kingsley Madikaegbu — https://www.linkedin.com/in/kmadikaegbu🔗 Alex Salkever / Agentic AI Foundation — linkedin.com/in/alexsalkever🔗 MCP Dev Summit North America — https://events.linuxfoundation.org/mcp-dev-summit-north-america/Timestamps:[00:00] Intro[00:13] AI Agent Liability[01:10] MCP in Healthcare AI[06:30] MCP vs REST Architecture[11:29] Healthcare Integration Challenges[18:29] Non-compliant Patient Challenges[24:13] Deterministic vs Model-Driven Workflows[28:08] AI in Healthcare Conversations[34:38] Agent-to-agent workflows in healthcare[38:02] Future MCP security

In this episode:🧠 Coding agents are generalist agents — why "positive transfer" means an agent that's better at code is better at everything, and how that makes them "AGI-complete"⏳ "Code will be solved in a year" — what the automation of knowledge work actually looks like, and why Jay joined ClickUp to be on it🏗️ Why the labs are crushing AI startups — free-for-two-years deals, Windsurf losing Claude access, and the brutal economics of building on top of frontier models🔗 The real moat is convergence — context, surfaces, and unit economics, a.k.a. "Cursor for your whole job"💬 Slack's data walls & the Glean problem — why fragmentation is the enemy and a single system of record wins🧪 RLVR & verifiability — why code became the perfect training ground for agents, and how to tell if you're even getting better🔬 LLMs are running the frontier of science — Putnam 12/12, Erdős problems, simulating a cell, and vibe-writing economics papers🚗 The car wash test that still breaks GPT-5 — spiky models, world models, Plato's cave, and the "stochastic parrot" debate🏖️ Plus: mechanistic interpretability as "brain surgery," catastrophic forgetting, the danger of deleting knowledge from models, and a pitch for a "resort for LLMs"Whether you're building agents, leading an AI team, or just trying to figure out what "agentic" really means for everyday work — this one's a fun, deep ride.🔗 Links & ResourcesJay Hack: linkedin.com/in/jayhackClickUp: clickup.comMLOps Community: go.mlops.communityMentioned: Gödel, Escher, Bach (Douglas Hofstadter) · "Machine Learning: The High-Interest Credit Card of Technical Debt" (Sculley et al.) · Periodic Labs · Ginkgo Bioworks · Physical Intelligence⏱️ Timestamps[00:00] AI Timeline[00:22] AI Startups and Timing[06:30] GPT-3 Impact[13:24] Selling CodeGen to ClickUp[19:31] AI Interaction Patterns[28:41] Slack and AI Agents[36:11] ClickUp Task Automation[41:32] AI in Scientific Research[48:48] Human Understanding vs AI[54:18] Catastrophic Forgetting Explained[59:59] AI Delegating to Humans[1:05:00] Agent-Based Game Integration[1:08:42] LLM vs Game Design[1:11:27] Wrap up#AIAgents #AgenticAI #ClickUp

Sam Partee (CTO & co-founder of Arcade.dev) and Nate Barbettini (Founding Engineer at Arcade.dev) sit down at the MCP Dev Summit to unpack what nobody wants to admit about the Model Context Protocol: the security model is still full of sharp edges. From tool poisoning and prompt injection to why OAuth got bolted onto the spec, this is a builder 's-eye view of where MCP breaks — and how to ship agents safely anyway.What we get into:🔓 OAuth on MCP — Why the spec adopted OAuth as its authorization standard, and the class of spoofing attacks it shuts down.☠️ Tool poisoning — How a malicious server hides instructions in tool descriptions, and why your agent trusts them by default.🧪 MCP Debugger & ToolBench — Shining a light on the rough edges by grading servers from S-tier to F-tier.🖥️ Sandboxing agents — Giving an agent a shell and a file system without handing over the keys to your machine.📜 Allow lists — Why MCP has client-level allow lists but skills mostly don't — and why that worries them.🔄 The auto-update problem — How skills and servers that silently update become a supply-chain risk ("rug pulls").✅ SOC 2, honestly — Why the controls are voluntary, misunderstood, and actually about best practices.🤖 AI-generated PRs — The new behaviors to watch for as agents start writing and merging code.If you build agents, ship MCP servers, or are responsible for AI security at your company, this one's for you.🔗 Links & ResourcesArcade.dev: https://www.arcade.devArcade MCP framework (GitHub): https://github.com/ArcadeAI/arcade-mcpSam Partee (GitHub): https://github.com/sparteeNate Barbettini (LinkedIn): https://www.linkedin.com/in/nbarbettiniMLOps.community: https://mlops.community⏱️ Timestamps[00:00] Skills, agents, and local context[08:36] MCP Debugger grades your server[10:34] Why AI clients are still buggy[20:54] Why agents shouldn’t always have shell access[22:44] “I have a spicy take.”[26:27] “Do not build your own auth.”[31:14] The “checking someone else’s email” problem[35:40] “OAuth is the best worst option.”[43:50] The future of AI entertainment[46:19] Tool poisoning explained[50:49] “Trust me, bro,” is not a security solution[52:45] MCP registries as the App Store model[1:00:28] AI-generated PRs and speed vs quality[1:02:37] Why behavior-driven development is coming back[1:08:11] Have we already reached AGI?#MCP #AIAgentSecurity #ToolPoisoning

Shahram Anver is the Co-Founder and CEO of Cleric, the autonomous AI SRE that investigates and root-causes production issues like an experienced teammate — often in under two minutes. Before Cleric, Shahram led MLOps, DevOps, and FinOps platform engineering at Gojek, Southeast Asia's super-app. In this conversation, he breaks down why production operations never kept pace with AI-accelerated development, and why the real unlock for an AI SRE isn't faster triage — it's an agent that *learns* and compounds operational memory across your whole org.In this episode:🔧 The on-call problem — Why one broken service still drags ten engineers onto a call, and how AI changes that🤖 What an AI SRE actually is — How Cleric investigates across your existing observability stack instead of adding another tool🧠 Learning over MTTR — Why Shahram argues the value isn't alert triage, it's an agent that gets better every investigation🪜 Ramping like a new engineer — Explore the environment, learn from the work, talk to the team🔁 The investigate–measure–learn loop — Turning what worked on one incident into context for the next🕸️ Knowledge graphs & operational memory — Mapping teams, clusters, and dependencies so insight from one team helps another⚡ Under two minutes to root cause — What "fast" really requires in a live production environment🚀 The road to autonomy — From assisted investigation toward self-healing infrastructureIf you're an SRE, platform engineer, DevOps lead, or anyone building or buying AI agents for production, this one's for you.🔗 Links & ResourcesCleric: https://cleric.aiShahram on LinkedIn: https://www.linkedin.com/in/shahramanver/Willem Pienaar (Co-Founder/CTO): https://www.linkedin.com/in/willempienaar/Cleric launches the first self-learning AI SRE: https://cleric.ai/blog/cleric-launches-the-first-self-learning-ai-sreMLOps Community: https://mlops.communityJoin the community: https://go.mlops.community/slack⏱️ Timestamps[00:00] Tech Jargon Confusion[00:27] Harness vs Model[08:48] Model Evolution in Cleric[13:36] Sandboxing and Simulated Environments[20:40] Shifting AI Perceptions[24:10] Managing Humans vs Agents[31:32] Steering Parallel Agents[34:16] Human Decision Integration in Models[43:28] 80/20 Data Split[49:40] Becoming a Skill[53:35] 2027 Agent Autonomy[59:14] Agent Learning in Production[1:04:31] Software as Personal Capabilities[1:08:31] Vibe Coding vs Durability[1:18:23] Wrap up#AISRE #SiteReliabilityEngineering #AIAgents

Zipline Roundtable episode: Building Real-Time ML Systems with Zipline + ChrononJoin the Community: https://go.mlops.community/YTJoinInGet the newsletter: https://go.mlops.community/YTNewsletterMLOps GPU Guide: https://go.mlops.community/gpuguideBig shout-out to ZiplineAI for the collaboration!// AbstractReal-time ML use cases like personalization and risk decisioning come with a unique set of challenges: serving fresh feature values at low latency for inference, generating temporally consistent backfills for training, and building complex chains of on-demand, batch, and streaming transformations. In this roundtable, practitioners from Intuit, CreditKarma, Depop, and OpenAI share how they use Zipline and the OSS Chronon project to solve these challenges and deploy real-time ML use cases in production.// BioGerman KrikorianGerman is a Software Engineer on the Feature Platform team at Credit Karma. Since joining the company during the early development of its recommendation system, they have played a key role in building and scaling the platform over the years. Their work focuses on feature pipelines and the feature store, which serves as critical infrastructure supporting numerous teams and business verticals across the organization.Ben MagyarBen is an engineer at Depop working on ML and data systems. Before Depop, he worked on Search at Etsy. Most of his work is around the infrastructure and operational problems that come with running ML systems at scale.Raj KatakamRaj architects ML Infrastructure at Credit Karma (Intuit). He holds a Master's in Software Engineering from Carnegie Mellon and a B.Tech in EECE from IIT Kharagpur. His interests include ML Infrastructure, Distributed Systems, Real-Time Data Processing, and Generative AI. His current focus is on providing feature engineering platforms, production GenAI infrastructure, vector databases, ML model serving, and MLOps pipelines for fraud detection, personalized recommendations, financial insights, and model explainability.Mick JermsurawongLed Flyte ML training/experimentation at Stripe, and now led Chronon for ML features at OpenAIHosted by Demetrios// Related LinksWebsite: https://zipline.ai/https://chronon.ai/~~~~~~~~ ✌️Connect With Us ✌️ ~~~~~~~Catch all episodes, blogs, newsletters, and more: https://go.mlops.community/TYExploreJoin our Slack community [https://go.mlops.community/slack]Follow us on X/Twitter [@mlopscommunity](https://x.com/mlopscommunity) or [LinkedIn](https://go.mlops.community/linkedin)] Sign up for the next meetup: [https://go.mlops.community/register]MLOps Swag/Merch: [https://shop.mlops.community/]Connect with Demetrios on LinkedIn: /dpbrinkmConnect with German on LinkedIn: /e2zdkwh8cxghydg/Connect with Raj on LinkedIn: /rajkiran2190Connect with Mick on LinkedIn:/mick-jermsurawong/

Naseem Al-Naji is the co-founder of MCPcat.io and the creator of Opal — a builder with deep roots in privacy-first developer tooling. In this conversation, he breaks down why MCP servers have become a black box in production, and how MCPcat gives teams X-ray vision into how agents and users actually behave.What we get into:🐱 What MCPcat Is — Open-source analytics and live debugging built specifically for MCP servers🎬 Session Replay — Watch an agent's full journey through your server, tool call by tool call🎯 Agent Intent & Goals — Understand "why" a tool was called, not just that it was🔍 Trace Debugging — Find exactly where agents and users get stuck or confused🚨 Catching Hallucinations — How issue tracking surfaces when an LLM goes off the rails🔒 Privacy-First by Design — Client-side redaction so sensitive data never leaves your environment⚡ One-Line Integration — Python, TypeScript, and Go SDKs that drop into existing stacks📊 Works With Your Stack — Native support for OpenTelemetry, Datadog, and Sentry🚀 The Future of MCP — Where agent observability and the MCP ecosystem are headingIf you build, ship, or maintain MCP servers — or you're trying to figure out why your AI agents misbehave in production — this one's for you.🔔 Subscribe, like, and share for more conversations on agentic AI:▶️ YouTube: https://www.youtube.com/@AAIFAgenticConversations🎧 Spotify: https://open.spotify.com/show/033rZZJrQOVSSmhcStFhZA?si=rUNjFuNqRvGvAEWwqms7TALinks & Resources:🐱 MCPcat: https://mcpcat.io💻 MCPcat on GitHub: https://github.com/mcpcat👤 Naseem on LinkedIn: https://www.linkedin.com/in/naseem-al-naji🐙 Naseem on GitHub: https://github.com/naji247Timestamps:[00:00] Intro[01:41] MCP Needs Gatekeepers[06:32] Measuring MCP Success[13:57] MCPAT Feature Rollouts[18:50] MCP Server Query Optimization[26:48] UI Design Shift[29:14] MCP Server Design Choices[33:51] User Journey Traceability[40:40] Agent Experience Evaluation[45:23] AI Model Improvement Strategies#MCP #AIAgents #Observability

Stanislas Polu is Co-Founder & CTO of Dust — the enterprise AI agent platform used by 51,000 workers at 3,000+ companies. Before Dust, he spent three years on OpenAI's research team under Ilya Sutskever, working on mathematical reasoning in language models, and prior to that was an engineer at Stripe. He brings a rare combination of frontier AI research and product-building experience to the enterprise agent space.Agents & the $40M Bet on Multiplayer AI // MLOps Podcast #384 with Stanislas Polu, Co-Founder & CTO of Dust🤖 What is Dust? — How Dust enables teams to build and deploy AI agents powered by internal company data, and why the "multiplayer AI" model is winning in enterprise.🧠 From OpenAI Research to Startup Founder — Stanislas's journey from studying mathematical reasoning in LLMs under Ilya Sutskever to co-founding an enterprise AI company in Paris with Gabriel Hubert.🚀 The $40M Series B — What Dust is building with fresh funding, the bet on human-agent collaboration as the future of work, and what "multiplayer AI" actually means in practice.🔄 The Outer-Loop Era — Stanislas's framework for thinking about where AI agents create the most value: not just automating tasks, but rewiring how work gets done across entire organizations.⚠️ What Most Enterprise AI Gets Wrong — The biggest mistakes companies make when deploying AI agents, why adoption fails, and how Dust achieves 70%+ weekly adoption rates.📊 Building Reliable Agent Infrastructure — Lessons from scaling to thousands of companies: observability, governance, data security, and why enterprise AI is harder than it looks.🛠️ Horizontal vs. Vertical AI Platforms — Why Dust chose to build a horizontal enterprise agent platform and how that decision shapes product, go-to-market, and technical architecture.This episode is essential for AI/ML engineers, enterprise AI leads, and anyone building or deploying AI agents at scale inside organizations.🔗 Links & Resources:• Dust: https://dust.tt• Stanislas Polu on X/Twitter: https://x.com/spolu• Dust on LinkedIn: https://www.linkedin.com/company/dust-tt• Dust $40M Series B announcement: https://dust.tt/blog• "The Outer-Loop Era" talk by Stanislas (dotconferences): https://www.youtube.com/watch?v=_outer_loop• Dust + Stripe MCP integration: https://stripe.com/customers/dust• Dust + Datadog observability case study: https://datadoghq.com/case-studies/dust⏱️ Timestamps [00:00] Future of Work[00:19] Dust Scaling Lessons[04:44] Human-Agent Collaboration[14:24] Pod as Workspace[22:30] Work Flow Optimization[29:37] Multiplayer Collaboration Vision[39:55] Token Economics and Inference[47:20] AI Pricing Challenges[52:36] Dust vs Co-work[57:06] Agentic Work Infrastructure[1:04:23] Stateful Sandbox Challenges[1:09:58] Product Use Case Discussion[1:14:05] Agent Data Interaction Needs[1:20:09] Wrap up#EnterpriseAI #AIAgents #Dust

James Everingham is the CEO and Co-founder of Guild.ai — the AI agent control plane for production teams. With roots at Netscape, Instagram (Head of Engineering), and Meta (Head of Dev Infra, leading a 1,000-person org), James brings rare, hard-won expertise to the challenge of operating AI agents at scale.From Single-Player to Multi-Player: Operating AI Agents at Scale // MLOps Podcast #383 with James Everingham, CEO and Co-founder of Guild.aiIn this episode, James unpacks what actually breaks when you move from a single AI agent to a fleet of them — and what engineering leaders need to build before it's too late.🎯 Single-Agent vs. Multi-Agent Systems — Why "single-player" AI workflows don't survive contact with production reality, and what the shift to multi-agent coordination actually demands from your infrastructure.🔍 The Agent Control Plane — What it is, why every engineering org needs one in 2026, and how Guild.ai is building the neutral layer to deploy, govern, and share agents across any framework or model.⚠️ Non-Determinism at Scale — Why AI agents behave like employees, not software, and why you need workforce-style governance — not just observability tooling — to manage them.💸 Token Spend & Cost Visibility — How teams running agents in production are flying blind on cost, and what Guild shows you that your current stack doesn't.🏗️ Lessons from Meta's DevMate — How Meta's AI coding agent went from experiment to submitting 50% of all diffs, and what that journey teaches every engineering leader about scaling agents safely.🚦 Agent Identity & Governance — Why every agent needs an identity, what happens when they don't have one, and how agent sprawl becomes a governance crisis fast.🔄 Sharing Agents as Infrastructure — Why Guild treats agents as shared production infrastructure rather than one-off scripts, and how that changes the economics of AI investment.🛠️ Framework Agnosticism — Why betting on a single agent framework is a losing strategy, and how to build for a multi-model, multi-framework world from day one.Essential viewing for engineering leaders, AI platform teams, and founders building production-grade agentic systems.🔗 Guild.ai: https://guild.ai🔗 James on X/Twitter: https://x.com/jevering🔗 James on LinkedIn: https://www.linkedin.com/in/jameseveringham🔗 Demetrios on LinkedIn: https://www.linkedin.com/in/dpbrinkm/⏱️ Timestamps [00:00] Context Transfer Challenges[00:51] Control Plane for Agents[02:17] Effective Agent Policies[09:23] Agent Governance Policies[15:34] Developer Tool Adoption[22:02] Knowledge Sharing and Open Source[24:59] Simulated Deployments and Confidence[29:36] Agent Workloads vs Human Workloads[39:55] AI as a Customer[47:59] Agent Hub vs Autonomy[53:21] Wrap up#AgenticAI #AIAgents #AIEngineering

Thiago Cardoso is the Director of Data & AI at iFood and the architect behind iFood Pago's AI agent platform. This fintech system serves millions of restaurants across Brazil through WhatsApp and the iFood app. In this episode, he breaks down what it actually takes to ship agentic AI in production at scale.The Control-vs-Magic Spectrum Building Agents // MLOps Podcast #382 with Thiago Cardoso, Director of Data & AI at iFood🤖 WHAT WE COVER:🔹 Control vs. Magic — Thiago's spectrum model for thinking about AI agents, from deterministic pipelines to fully autonomous systems🔹 iFood Pago Explained — How iFood's embedded fintech arm uses AI agents to provide credit, loans, and financial services to restaurants🔹 WhatsApp as an AI Interface — Why WhatsApp is the primary channel for merchant interactions in Brazil and how agents are deployed there🔹 Multi-Agent Architecture — Why single monolithic agents break down and how to split them into sub-graphs with specialized contexts and tool sets🔹 Context Engineering — Why what you put in the agent's context window is more important than the model itself🔹 Human-in-the-Loop Design — How to build trust with merchants while minimizing friction in agentic workflows🔹 LangGraph in Production — How Thiago's team uses LangGraph to build stateful, multi-agent pipelines🔹 Debugging with AI — Generating on-the-fly HTML/JavaScript visualization tools to investigate data pipeline problems🔹 The Cost of Software Going to Zero — What happens to demand when software becomes nearly free to build🔹 Personalization at Scale — Serving millions of restaurants with AI that knows their business context🎯 This episode is for AI engineers, ML practitioners, and fintech builders who want to understand what production agentic AI looks like beyond the demos.🔗 LINKS & RESOURCES:Thiago Cardoso on LinkedIn: https://www.linkedin.com/in/thiagoncc/iFood: https://www.ifood.com.briFood Pago: https://ifoodpago.com.brZenML iFood Case Study: https://www.zenml.io/llmops-database/building-a-hyper-personalized-food-ordering-agentLangGraph: https://www.langchain.com/langgraph⏱️ TIMESTAMPS[00:00] Control vs Magic in AI[00:18] Foodpago Fintech Ecosystem[08:59] Scaling Personalization with AI[15:04] Chat UI Evolution[20:22] Context Layer in Systems[26:39] Agent Growth Dynamics[33:39] Job Evolution with Open Claude[39:54] AI and Software Costs[41:50] Wrap up#AIAgents #Fintech #iFood

Sherwood Callaway is the founder of Sazabi (YC P26), the AI-native observability platform built for engineering teams who ship fast. He previously founded and exited a YC company — now he's back, betting that logs are all you need to replace Datadog.Logs Are All You Need: Rethinking Observability with AI Agents // MLOps Podcast #381 with Sherwood Callaway, the Founder of Sazabi🔑 What's covered:🪵 Logs vs. The Three Pillars — Sherwood makes the case that the traditional observability stack (metrics, logs, traces) is overkill. In 2026, with AI agents in the loop, logs alone are sufficient — and dramatically simpler to instrument.🚨 AI-Generated Alerts, Not AI-Evaluated Alerts — Instead of using AI to triage your noisy alert stream, Sazabi generates the alerts autonomously from your logs and codebase — so you never configure a monitor again.🤖 Agent Sandboxing & Bash Access — How Sazabi gives its AI agent a persistent bash sandbox with CLI tool access, why every other action routes through that sandbox, and how RLS database permissions keep the agent from doing damage.🧠 Agentic Memory via Git — Sazabi's novel approach to persisting agent memory across threads using Git branches — enabling multiple parallel sub-agents to share findings without bloating the context window.🔀 Multi-Agent Parallelization — How Sazabi spawns sub-agents and background agents on-demand to investigate production issues in parallel, the way Claude Code displays a live to-do list of agent work.📊 Why Evals Are Hard (and What They Built Instead) — An honest conversation about the difficulty of evaluating agentic systems, log-based eval proxies, and why Sazabi still doesn't buy third-party eval tooling.⚡ MCP Servers, Skills Bloat & Context Management — The tradeoffs between MCP servers and local skill files, progressive tool disclosure, and why context window management is the hidden bottleneck in production agent systems.🎯 Building a Moat in 2026 — Sherwood and Demetrios debate what a defensible advantage actually looks like when every AI tool can be cloned fast. Spoiler: "We built it first" is not a moat.🚀 Beta Launch & Who It's For — Sazabi is in closed beta and opening the waitlist. If your team uses Cursor or Claude Code and you have production traffic you can't afford to break, this is built for you.👉 Perfect for: AI engineers, SREs, DevOps teams, and founders building production-grade agent systems who are questioning whether their current observability stack is overbuilt.🔗 Links & Resources🌐 Sazabi: https://sazabi.com📄 Sazabi on Y Combinator: https://www.ycombinator.com/companies/sazabi💼 Sherwood Callaway on LinkedIn: https://www.linkedin.com/in/sherwood-callaway📰 SiliconANGLE coverage: https://siliconangle.com/2026/04/08/startup-sazabi-bets-on-logs-and-ai-agents-to-replace-traditional-observability-stacks/💻 MLOps.community: https://mlops.community⏱️ Timestamps [00:00] Genetic Agent Evolution[00:33] Dethroning Datadog[03:13] Sazabi vs Traditional Observability[10:47] MCP vs CLI Paradigm[15:12] Sandbox Usage for Agents[24:28] Genetic Prompt Optimization[32:34] Eval and Agent Spawning[38:45] RL Environment Tensions[45:40] Sazabi is hiring![46:10] Wrap up#Observability #AIAgents #DevTools

Joe Maionchi (Co-founder & COO) and Rod Christensen (Co-founder & Chief Architect) of RocketRide join the MLOps Community to walk through AIDE — the AI Integrated Development Environment. RocketRide is an open-source AI pipeline platform that lets developers build, debug, and run production-grade agentic AI workflows directly from their IDE, with support for 13+ LLM providers, 8+ vector databases, and full multi-agent orchestration.AI Is Fast. AI Projects Are Slow. Let's Fix That. // MLOps Podcast #378 with JRocketRide's Joe Maionchi (Co-founder & COO) and Rod Christensen (Co-founder & Chief Architect)A huge shout-out to  ⁨RocketRide⁩  for this collaboration!🔑 What's covered:🏗️ Why AI infrastructure needs standardization — how coding agents produce inconsistent "glue code" across projects and why a typed node graph fixes it⚡ Efficiency AI vs. Opportunity AI — the two paths companies take with generative AI, and which one actually compounds growth🔀 Multi-agent pipeline orchestration — running CrewAI, LangChain, and DeepAgent side-by-side to benchmark which works best for your use case💰 Cutting LLM costs in half — design-time strategies for routing tasks to cheaper models without sacrificing output quality🔍 Pipeline observability & debugging — logging every node step in dev and production so you can pinpoint exactly where a 10-step pipeline breaks🖼️ Beyond text: image, video & audio nodes — frame grabbing, OCR, Whisper transcription, and speech-to-text running on shared GPU infrastructure🚀 RocketRide Cloud — one-click deploy from local to cloud with dynamic GPU scaling and cost-efficient shared inference🧠 Intentionality in agentic development — why moving fast with AI agents creates "crappy code fast" and how skills/context files change the equation🔌 MCP support & framework-agnostic design — swap any model, tool, or framework without rewritesThis episode is essential for AI engineers, ML practitioners, and developers building production LLM applications who want to stop reinventing infrastructure and start shipping.🔗 Links & Resources:• RocketRide website: https://rocketride.ai• RocketRide open source (GitHub): https://github.com/rocketride-org/rocketride-server• AIDE VS Code Extension: https://rocketride.org• MLOps Community: https://mlops.community• Discord: https://discord.gg/Hd4PukFt2H⏱️ Timestamps[00:00] Cost Savings in AI[00:21] AI, Developer, and Software Development Evolution[02:51] Intentionality in Software Development[10:51] Model Skill Optimization[17:08] Primitives in AI Systems[29:00] Coding Agent Challenges[37:09] RocketRide Inspiration[44:42] Coding Agents and Documentation[47:40] RocketRide Cloud Overview[56:27] Wrap up

BuzzHPC Roundtable episode: Architecting Modern AI Systems: Platforms, Agents, and Integration Join the Community: https://go.mlops.community/YTJoinInGet the newsletter: https://go.mlops.community/YTNewsletterMLOps GPU Guide: https://go.mlops.community/gpuguideBig shout-out to BuzzHPC for the collaboration!// AbstractAs AI systems evolve into more autonomous, agent-driven architectures, the way we design platforms, tools, and infrastructure is rapidly changing. In this session with BuzzHPC, we explore the shifting boundary between platforms and tools, what developers expect platform providers to handle versus what they want to control and build themselves. We unpack what modern agentic stacks look like today, how teams are structuring them in production, and where these architectures are heading as systems become more complex and distributed. A key focus will also be on agent interoperability, how different agents communicate, coordinate, and operate within shared environments.Finally, we share insights and lessons from a recent AI hackathon delivered in partnership with Bell, Buzz, Mila, and KHP, highlighting how these concepts are being tested and applied by builders in real-world scenarios.// BioAllen RoushAllen has held senior technical and AI leadership roles at companies like Oracle and Intel. He's very active in the AI research space and open source communities. He's passionate about improving the creativity and coherence of AI systems.Frédéric BénardFrédéric is Senior Director of AI Applications Development at Mila (Quebec AI Institute), where he leads a team focused on building the engineering foundations for applied AI systems. His work centers on translating cutting-edge research into scalable applications, including AI-driven platforms and agent-based systems used across research and industry collaborations.Shuo WangShuo leads the Responsible AI Office for Bell Canada, where all AI use cases are reviewed and assessed for potential harm and bias. Previously, he led a team of data scientists to expand a large-scale ML program to improve customer support effectiveness.// Related LinksWebsite: https://www.buzzhpc.ai/~~~~~~~~ ✌️Connect With Us ✌️ ~~~~~~~Catch all episodes, blogs, newsletters, and more: https://go.mlops.community/TYExploreJoin our Slack community [https://go.mlops.community/slack]Follow us on X/Twitter [@mlopscommunity](https://x.com/mlopscommunity) or [LinkedIn](https://go.mlops.community/linkedin)] Sign up for the next meetup: [https://go.mlops.community/register]MLOps Swag/Merch: [https://shop.mlops.community/]Connect with Demetrios on LinkedIn: /dpbrinkmConnect with Allen on LinkedIn: /allen-roush-27721011b/Connect with Frédéric on LinkedIn: /benard/Connect with Shuo on LinkedIn: /shuow/

Big news: the MLOps Community is joining the Linux Foundation to become the official user community of the new Agentic AI Foundation (AAIF). The AAIF is the neutral home for open source projects like the Model Context Protocol (MCP), goose, and AGENTS.md, co-founded by Anthropic, Block, and OpenAI. With that governance and scaffolding now in place, the open source agent ecosystem has room to scale, and the MLOps Community is right in the middle of it.Everything you love about the community from the past six years keeps going, and we are adding even more on top.What this means:- Official user community: MLOps Community becomes the user community of the Agentic AI Foundation under the Linux Foundation.- The projects: MCP, goose, and AGENTS.md now live under one open, neutral governance structure built to scale.- Nothing goes away: The podcast, the global meetups, the weekly newsletter, the Slack workspace, and the virtual events all continue.- New: Ambassador Program: Just opened for applications, so you can get more involved in the community.- AgentCon EU: September 17 and 18 in Amsterdam.- AgentCon North America: October 22 and 23 in San Jose.- A possible new name: The podcast may become "Agentic Conversations," because honestly all we talk about is agents. Tell me what you think in the comments.If you build with AI agents or follow the open source agent ecosystem, this is the update to bookmark. This is MLOps Community 2.0.Links and Resources:- MLOps Community: https://mlops.community- MLOps Community 2.0: https://mlops.community/blog/mlops-community-2-0- Agentic AI Foundation: https://aaif.io- Ambassadors: https://aaif.io/ambassadors- Linux Foundation AAIF announcement: https://www.linuxfoundation.org/press/linux-foundation-announces-the-formation-of-the-agentic-ai-foundation- AgentCon and MCPCon events: https://events.linuxfoundation.org/aaif-events/- Model Context Protocol (MCP): https://modelcontextprotocol.io- goose: https://goose-docs.ai- AGENTS.md: https://agents.mdTimestamps (approximate, adjust before publishing):00:00 The big announcement00:12 Joining the Linux Foundation's Agentic AI Foundation00:30 Why it matters: MCP, goose, and AGENTS.md00:48 What is not changing: podcast, meetups, newsletter, Slack01:15 What is new: the Ambassador Program01:30 AgentCon EU in Amsterdam and North America in San Jose01:55 A new name for the podcast: Agentic Conversations?02:10 MLOps Community 2.0#AgenticAI #MCP #LinuxFoundation

Guthrie Cooper (Senior Group Product Manager, AI & Robotics) and Nidhi Sharma (Global Head of Engineering AI & Incubation) from Just Eat Takeaway.com join the MLOps.community to pull back the curtain on how one of Europe's largest food delivery platforms is running an internal innovation engine. From autonomous delivery robots to agentic AI voice assistants, they share what it actually takes to build like a startup inside a 40,000-person company.Inside Just Eat's AI Lab: Voice Agents & Agentic Commerce // MLOps Podcast #377 with Just Eat Takeaway.com's Guthrie Cooper (Senior Group Product Manager, AI & Robotics) and Nidhi Sharma (Global Head of Engineering AI & Incubation)🤖 Delivery Robots — How JET partnered with RIVR and DELIVERS.AI to deploy physical AI ground robots in Zurich, Milton Keynes, and Bristol, and what the first pilots taught the team🧠 AI Incubation at Scale — How Nidhi's team built a dedicated incubation unit to fast-track AI experiments without the red tape of a large enterprise🎙️ AI Voice Assistant — The story behind JET's new voice-first food ordering experience, and the ML challenges of building a conversational concierge at scale🦾 Physical AI vs. Software AI — Why deploying wheeled-legged robots in real cities is fundamentally different from shipping a model update, and the MLOps implications🚀 Corporate Innovation Playbook — The frameworks Guthrie and Nidhi use to move from idea to pilot in weeks, not quarters, inside a large org📦 Innovation as a Platform — How JET is thinking about turning its delivery infrastructure and AI capabilities into a reusable platform for new business lines🔗 Startup Partnerships — What makes a good external innovation partner (vs. building in-house), and how JET evaluates robotics and AI startups for pilots⚡ Agentic AI & Accessibility — How agentic AI is being used to make food ordering genuinely accessible for blind and low-vision usersWhether you're an ML engineer at a large company trying to get AI into production, a product leader navigating corporate innovation, or a startup founder looking to partner with a platform player — this conversation is packed with practical lessons.🔗 Links & Resources:Just Eat Takeaway.com: https://www.justeattakeaway.comRIVR (physical AI delivery robots): https://www.rivr.aiDELIVERS.AI (UK delivery robots): https://www.delivers.aiProsus (JET parent company): https://www.prosus.comMLOps.community: https://mlops.community⏱️ Timestamps[00:00] AI Innovation Incubator Strategy[03:16] Everyday Convenience Expansion[07:03] Context Ownership in Ecosystems[17:35] LLM Integration and Discovery[24:02] Whoop Notifications Grievances[33:01] Expanding Beyond Food[48:20] Innovation Lab Failures[51:22] Rory Sutherland's Alchemy[1:03:23] Latency and Conversational Design[1:13:42] Drone Delivery Efficiency[1:18:06] Wrap up#AgenticCommerce #VoiceAI #DroneDelivery

Pramod Krishnan is a Managing Director - AI Managed Services at PwC, specializing in enterprise AI transformation — helping large organizations move from AI experimentation to production operating models. In this episode with Demetrios, Pramod breaks down exactly what the OpenClaw wave means for enterprises, and the control frameworks PwC uses before a single agent touches production.Huge thanks to ⁠PwC⁠ for supporting this episode!Autonomous Agents at Work: From OpenClaw Hype to Enterprise Reality // MLOps Podcast #378 with Pramod Krishnan, Managing Director - AI Managed Services at PwC US.🔑 OpenClaw & the Agentic Hype Cycle — Why the fastest-growing open-source agent project in history (190K+ GitHub stars in weeks) is a forcing function for enterprise AI governance, and what most organizations are getting wrong.🏗️ 3-Tier Work Classification — Pramod's framework for categorizing any agentic task as reversible, sensitive, or consequential — and how the approval gates, controls, and blast radius differ for each tier.🛡️ The Guardrails Stack — A concrete list of non-negotiable guardrails: allow-listed tool calls, prompt injection defense, credential protection, toxic output filtering, and more — straight from PwC's production deployments.🔍 5-Part Auditability Framework — How to make AI agents truly auditable across quality (LLM-as-judge), performance, safety, cost, and security — and why OpenTelemetry alone isn't enough.💰 Agent Cost & ROI Tracking — Why successfully deployed agents are generating the hardest financial measurement problems enterprises have ever faced, and what a real cost-tracking architecture looks like.🔒 Agent Security in Depth — From API key harvesting attacks to credential leakage to malicious actor scenarios: what security controls PwC requires before any agent goes live.⚙️ The Minimum Control Stack — The non-negotiables Pramod would walk in with on a Monday before clearing any agent for production: what they are, why they matter, and how to implement them.🔄 Human-in-the-Loop Design — The difference between "human in the loop" (approves every action) and "human on the loop" (monitors and intervenes) — and how to choose the right pattern based on consequence level.🤝 AI as a Force Multiplier — How Pramod thinks about AI ownership, intellectual authorship, and making sure humans remain deliberate and responsible even as agents accelerate output.This episode is essential for ML engineers, platform architects, CIOs, and AI product managers who are moving beyond demos into real enterprise agentic deployments.🔗 Links & ResourcesPramod Krishnan on LinkedIn: https://www.linkedin.com/in/pramod-potti-krishnan/MLOps.community: https://mlops.communityOpenClaw project: https://openclaw.aiBCG on OpenClaw + Enterprise: https://www.bcg.com/publications/cios-openclaw-and-the-new-wave-of-ai-agentsPwC 2026 AI Business Predictions: https://www.pwc.com/us/en/tech-effect/ai-analytics/ai-predictions.htmlTimestamps:[00:00] AI in Enterprise[02:04] AI System Failures[08:01] Agent Decision Tracing[13:07] Agent Design Tension[16:21] Agent Control Stack Essentials[20:20] LLM Cost and FinOps[26:16] Agent Attack Surfaces[30:00] Tools as Attack Vectors[33:47] Human in the Loop[37:00] AI Ownership and Accountability[41:42] Wrap up. Shoutout to Pramod and PwC!

Hamza Tahir, co-founder of ZenML, joins the show to cut through the hype around long-running agents — arguing that at the end of the day, an agent is just a while loop that talks to a model, calls a tool, and writes to a file system. He covers the architecture of agent harnesses (inner and outer), what durable execution actually guarantees (and what it doesn't), and why the ML pipeline paradigm is a cleaner mental model than transactions for most agent workloads.Hamza also announces Kitaru — ZenML's new open-source execution runtime for async Python agents — built on five years of running ML workloads in enterprise environments.What we get into:Agents are while loops: The surprising simplicity under all the tooling: a brain (LLM), hands (tool calls), and a file system, stacked recursivelyInner harness vs outer harness: Why Pydantic AI owns the inner loop while production deployment needs a separate runtime layerWhat "long-running" actually means: Why the infrastructure we need to build is about extrapolating the future, not defining a time window todayDurable execution demystified: What checkpointing actually guarantees (infra failures, pod death, network drops) vs. what it never will (external state, bad LLM outputs, Snowflake rollbacks)ML pipelines vs transactions: Why bursty containers in Kubernetes map more naturally to agent workloads than microsecond-latency queue workers — and why Hamza argues against the complexity taxAnthropic opening the harness: Why letting other models run Claude Cowork is a "boss move," and what it means for the one-harness vs one-model debateHuman-in-the-loop, done right: The pod-kill-and-resume pattern, and why warm pools matter less when your agent runs for daysKitaru: ZenML's new open source durable execution runtime: zero-config local, Kubernetes/SageMaker/Vertex in production, built on Pydantic AI integrationArguing with Claude about Temporal: Hamza's story of spending hours getting an LLM to admit ZenML and Temporal solves the same problemIf you're architecting agents for production, picking between Pydantic AI, LangGraph, and Temporal, or just want to understand what "durable execution" actually means — this is the episode.// LINKS & RESOURCESKitaru on GitHub: https://github.com/zenml-io/kitaruKitaru launch blog post: https://www.zenml.io/blog/kitaru-launchKitaru on Hacker News: https://news.ycombinator.com/item?id=47520115Hamza Tahir on LinkedIn: https://www.linkedin.com/in/hamzatahirofficial/ZenML: https://www.zenml.io/ Timestamps[00:00] While Loop Checkpointing[00:24] Long-Running Agents Explained[01:28] Agent Harness Model Definitions[06:30] Durability and State Recovery[11:03] Agent Systems Layers[18:45] Durability in Agent Systems[22:07] ML Pipeline vs Transactions[29:23] Durability vs Guarantees[33:13] Durability vs Chaos Engineering[39:50] Kitaru Naming and Purpose[40:38] Wrap up#AIAgents #DurableExecution #OpenSource

Rafael (Head of Innovation, iFood) and Daniel (Data and AI Manager, iFood) pull back the curtain on ILO-Agent — iFood's conversational AI ordering system built for 200 million users across Latin America. Recorded live at AI House Amsterdam, this conversation goes deep into the engineering and product decisions behind building recommendation systems and agentic AI, and why the speed of your AI's response might actually be destroying user trust.The Latency Goldilocks Zone Explained // MLOps Podcast #376 with iFood's Rafael Borger (Head of Innovation) and Daniel Wolbert (Data and AI Manager)🍕 Recommendation Systems at Scale — Why personalizing for 200M users with wildly different food tastes, budgets, and cultures is a fundamentally different problem than standard ML🤖 ILO-Agent Deep Dive — What iFood's conversational AI agent actually does, how it handles open-ended requests ("a romantic dinner for two, my wife hates onions"), and where it's headed⏱️ The Latency Goldilocks Zone — The fascinating insight that LLM responses can be too fast (users don't trust them) or too slow (users abandon) — and how to find the sweet spot🧠 Perceived vs. Actual Latency — Why showing progress indicators and partial results can make a 6-second response feel instant, and how iFood uses this in production🛒 The Tinder for Food Experience — How iFood is experimenting with swipe-based discovery to solve "I don't know what I want to eat" for millions of undecided users🗣️ Voice vs. Text AI Interfaces — Why voice ordering limits you to 6 items in 30 seconds, and why text-based agents need radically different output design🔗 Agent-to-Agent (A2A) Architectures — What happens when your customer support agent and your ordering agent need to collaborate, and the standardization challenges ahead📊 Measuring Product-Market Fit for AI — Why the Sean Ellis / Chanel score method breaks down in Brazil, and what iFood uses instead🏗️ Scalability vs. Ecosystem Health — The real tension between consuming partner APIs aggressively and keeping the food delivery ecosystem sustainable🌎 Building AI for Global-Local Markets — Why one-size-fits-all AI products fail and how iFood builds for cultural and economic diversity simultaneously. This episode is for ML engineers, AI product managers, and data scientists building production AI systems at scale — especially if you're working on recommendation, retrieval, or agentic systems in consumer apps.🔗 Links & ResourcesMLOps.community: https://mlops.communityAI House Amsterdam: https://aihouse.amsterdamiFood: https://www.ifood.com.br/iFood AILO launch coverage: https://tiinside.com.br/en/10/10/2025/ifood-lanca-ailo-assistente-de-ia-que-inaugura-pedidos-por-conversa/iFood AI case study (AWS): https://aws.amazon.com/solutions/case-studies/ifood-bedrock/Related MLOps Community talk — "From Zero to AILO" by Nishikant Dhanuka & Chiara Caratelli: https://home.mlops.community/public/videos/from-zero-to-ailo-lessons-learned-from-building-ifoods-ai-agent-nishikant-dhanuka-and-chiara-caratelli-2025-11-25ZenML LLMOps database write-up on iFood's hyper-personalized agent: https://www.zenml.io/llmops-database/building-a-hyper-personalized-food-ordering-agent-for-e-commerce-at-scale⏱️ Timestamps[00:00] Recommending the unknown[00:18] Ailo Hyperpersonalization Insight[06:24] Predictive Personalization Insights[09:13] "Jet skis" of innovation[17:45] Consumer Behavior and Chatbots[26:33] Perceived Latency and Engagement[33:22] AI-driven UI Evolution[38:17] LCM Voice Mode Inquiry[45:20] Chat as Interface[47:46] Wrap up

Nicolas Alejandro Bogliolo is the AI PM at Despegar, the largest online travel agency in Latin America, and the engineer-product-hybrid behind Sofia, the GenAI travel concierge that beat most of the OTA world to a working multi-agent system. Before MCP was a standard and before LangChain was widely adopted, his team had already shipped their own orchestration layer and tool protocol in production. This conversation is a rare look at what it takes to build an agentic system that actually books trips, runs on WhatsApp, and keeps adding capabilities without falling over.Building MCP Before MCP Existed: Inside Despegar's Sofia Agent // MLOps Podcast #375 with Nicolas Alejandro Bogliolo, AI PM at DespegarWhat we cover:- Chappi, the brain of Sofia: how Despegar built an internal orchestration layer when there was nothing off the shelf- Building "MCP before MCP": the custom tool-calling protocol that predated the Anthropic standard- Multi-agent architecture by vertical: flights, hotels, activities, and cars each own their own flow- Decentralized agent ownership: how any squad in the company can build a flow with central supervision- Sofia on WhatsApp: making messaging the consumer control center, the way Slack became it for the enterprise- The five-phase travel arc Sofia covers: dreaming, planning, anticipation, in-trip, and post-trip- KPI evolution: why "in-scope conversation rate" topped out near 96 percent and what they measure now- The flight-delay-claim use case and why filing claims through a chatbot is a perfect agent task- Group trip planning in WhatsApp groups: the next frontier for travel agents- Sofia as channel of choice: the WeChat-style vision for an agent that handles your entire trip- Why Despegar held off on giving Sofia the ability to bargain with customers, for now. Whether you are building production agents, running an OTA, or just curious about how an AI travel concierge actually works under the hood, this episode is full of grounded, in-production lessons from a team that had to invent the patterns the rest of us are now adopting.Links and Resources:Despegar: https://www.despegar.comSofia announcement: https://investor.despegar.com/news-presentations/news-releases/news-details/2024/Despegar-revolutionizes-the-tourism-industry-introducing-the-regions-first-Generative-AI-Travel-AssistantSofia coverage on PhocusWire: https://www.phocuswire.com/despegar-debuts-genai-travel-assistant-remembers-previous-interactionsMLOps Community: https://mlops.communitySubscribe for more agent and AI infra deep divesTimestamps [00:00] Sophia Travel Concierge AI[00:38] Sophia Multi-Agent System[06:00] AI Limitations in Practice[13:52] Travel Planning Exploration[18:03] Group Travel Decision Making[21:32] Agent Ecosystem Design[30:14] Sofia's Travel Assistant Vision [33:35] Orchestration and MCP Design[40:13] Sophia Negotiation Concerns[40:47] Wrap up#AIAgents #MCP #AgenticAI

This episode is brought to you by the MLflow team. Check out more information at MLflow.org.What does it actually take to build voice AI at a billion-interaction scale? This episode features an ex-Amazon voice AI engineer who built customer support systems handling 2 billion+ interactions — now working on next-gen voice agent platforms. Anurag digs deep into the real engineering tradeoffs, design patterns, and use cases that separate production-grade voice agents from demos.Voice Agent Use Cases // MLOps Podcast #374 with Anurag Beniwal, Member of the Technical Staff at ElevenLabs🎙️ Topics covered:🔹 Cascaded vs. speech-to-speech — Why cascaded systems still win in production, and how to make them feel natural without sacrificing control🔹 Latency masking — Foreground/background model architecture and how to buy yourself time while deep retrieval runs🔹 Constellation of models — Using Haiku for tool calling, fine-tuned smaller models for response generation, and why "one model for everything" breaks at scale🔹 Turn-taking & ASR challenges — Why voice is harder than chat: accents, noise, silence detection, and domain-specific fine-tuning🔹 Level 1 vs Level 2 customer support — Why today's agents max out at Level 1 and what it takes to capture Level 2 expert judgment🔹 Inbound vs. outbound sales agents — Where voice agents are already winning, and why inbound lead qualification beats cold outbound🔹 Booking, reservations & concierge — The clearest near-term wins for voice agents across hospitality, home services, and SMBs🔹 Continual learning from natural language feedback — How to build agents that improve from real operator feedback without ML expertise🔹 Conversational TTS — Why passing full conversation history to your TTS model changes everything for tone consistency🔹 User tiers for voice platforms — Non-technical business owners vs. developers vs. enterprise: why one interface doesn't fit all. If you're building production voice agents, evaluating voice AI vendors, or scaling AI-first customer support — this episode is packed with hard-won lessons from someone who's done it at Amazon scale.🔗 Links & Resources:MLOps.community: https://mlops.communityGoogle Scholar: https://scholar.google.com/citations?user=g_QB5WgAAAAJ&hl=en&oAmazon science page: https://www.amazon.science/author/anurag-beniwalJoin the Community: https://go.mlops.community/YTJoinInGet the newsletter: https://go.mlops.community/YTNewsletterMLOps GPU Guide: https://go.mlops.community/gpuguide⏱️ Timestamps [00:00] Cascaded Systems Control Challenge[05:35] Voice vs Chat Complexity[14:16] MLflow's open source platform[15:03] AI Model Constellations[23:00] Model Constellations Use Cases[31:40] Voice vs Text Context[33:54] Voice as Thought Capture[42:11] Cascaded vs Speech-to-Speech Debate[50:02] Wrap up

Jesse Vincent is the Founder & CEO of Prime Radiant and creator of Superpowers — the most-used Claude Code plugin in the world. He built the first agentic software development methodology from scratch while managing MIT interns in the early 2000s, and hasn't written a line of code manually since October.The Creator of Superpowers: Why Real Agentic Engineering Beats Vibe Coding // MLOps Podcast #373 with Jesse Vincent, Founder & CEO of Prime RadiantIn this conversation, Jesse walks Demetrios through the full Superpowers system: why he thinks most developers are still approaching agentic coding wrong, how he designs skills that force LLMs to stop rationalizing and actually follow rules, and what he's building next at Prime Radiant — including Green Field, an unreleased tool for reverse-engineering legacy codebases into specs. This one is for developers who want to go beyond "vibe coding" and build AI-assisted workflows that actually scale.🔧 Topics Covered🧠 The Superpowers Methodology — How the brainstorming skill extracts what you actually want before you hand work to an agent, and why most developers skip this step📋 Spec-Driven Development & Plan Files — Why Jesse insists on TDD, DRY, and YAGNI for every agentic task, and how planning skills generate per-task context blocks agents can actually execute on🐛 Debugging with Agents — Jesse's systematic approach to root cause analysis, reproduction cases, and the 30 years of debugging instinct he's baked into a skill🔄 Pressure Testing LLM Skills — How Claude fires up sub-agents and stress-tests its own rules to catch rationalization before it shows up in production🛠️ Clearance IDE — Jesse's new Markdown-native development environment built for humans working alongside AI, with a history pane for file navigation📦 Green Field (Unreleased) — A toolset for turning old codebases or built products into clean specs — not yet public but dropping soon from Prime Radiant🧑‍💼 Management as the Magic Trick — Why the real unlock of tools like Superpowers is that they make every developer a manager, and why that transition is hard the first time⚖️ Software Ethics in the Agent Era — Reverse engineering, license washing, open source cloning, and whether the value of software itself is collapsing🔗 Links & ResourcesPrime Radiant: [https://prime-radiant.com](https://prime-radiant.com/)Superpowers on GitHub: https://github.com/prime-radiant-incClearance IDE: https://github.com/prime-radiant-inc (check repo)MLOps.community Slack: https://go.mlops.community/slackMLOps.community website: [https://mlops.community](https://mlops.community/)⏱️ Timestamps[00:00] Greenfield Toolset Insights[00:27] Superpowers Kit Evangelism[08:06] Hyperbolic's GPU Cloud[17:48] Debugging Skill Creation[22:12] Skill Extraction Strategy[31:15] Smallest Harness[41:06] Software supply chains[48:56] Visual Precision Challenges[54:09] Creative Feedback Loops[1:04:24] MLflow's Gen AI[1:05:55] Wrap-up

Maggie Konstanty is an AI Product Manager at Prosus, one of the world's largest consumer internet companies, where she builds and evaluates AI agents for food ordering and ecommerce at scale. She's been inside the messy reality of LLM evaluation longer than most — and her take is unfiltered.It's 2026, and We're Still Talking Evals // MLOps Podcast #372 with Maggie Konstanty, AI Product Manager at Prosus🧪 Why accuracy metrics lie — Maggie breaks down why "95% accurate" tells you almost nothing about whether your agent is actually working in the real world, and what to measure instead.🏗️ Pre-ship vs. production evals — Your eval suite before launch will not survive first contact with real users. Maggie explains the structural disconnect and how to close the gap.👻 The silent failure: user drop-off — Users who are unhappy don't complain — they just leave. Discover why drop-off analytics are one of the most underutilized eval signals in production.🎯 Instruction to fail: the 20-evaluator trap — Setting up 20 types of evaluators not connected to your product goal is a fast path to wasted time. How to design evals that are tied to real outcomes.🍽️ The "surprise me" edge case — A real example from Prosus's food ordering agent and what it reveals about how users actually behave vs. how PMs imagine they do.🤖 LLM-as-a-judge: the limits — Why Maggie doesn't lean on LLM-as-a-judge for accuracy measurement, and what approaches she uses instead for production-grade evaluation.🛠️ Arize/Phoenix & eval tooling critique — A candid take on the current state of eval platforms, why she spent a whole day fighting the UI, and why mature teams often go back to custom code.🧬 Eval as team DNA — Evals aren't a launch checklist. Maggie makes the case that they need to be a constant practice embedded in team culture — and why alignment on "what good looks like" is harder than any technical implementation.🔢 When to stop optimizing — What happens when your eval score approaches 100%, and how to know when it's time to shift focus to a different metric or flow.💬 Red teaming with incentives — A fun tactic: running adversarial eval sessions where engineers compete to break your agent for an Amazon gift card.This is required watching for AI PMs, ML engineers, and applied AI teams who have moved past "getting evals set up" and are now struggling with making them actually matter.---🔗 Links & ResourcesMaggie Konstanty on LinkedIn: https://www.linkedin.com/in/maggie-konstantyProsus: [https://www.prosus.com](https://www.prosus.com/)MLOps.community: [https://mlops.community](https://mlops.community/)Arize AI / Phoenix (mentioned): [https://arize.com](https://arize.com/) / [https://phoenix.arize.com](https://phoenix.arize.com/)MLOps.community Slack: https://go.mlops.community/slack⏱️ Timestamps[00:00] Evaluations and User Alignment[00:18] Eval Lifecycle in Production[06:05] LLM Accuracy and Judging[15:30] Evals vs Tests in AI[22:39] Profanity as Frustration Signal[29:23] Impact-weighted performance[32:22] Eval Tooling Pros and Cons[38:10] Build vs Buy Dilemma[39:35] Wrap up

This episode is brought to you by Hyperbolic and the MLflow team. Check out more information at hyperbolic.ai and MLflow.org.Why AI Coding Agents Are Moving to the Cloud — With Zach Lloyd, CEO of WarpZach Lloyd is the founder and CEO of Warp, the AI-native terminal and agentic development platform trusted by over a million developers. Before Warp, Zach was a product lead at Google on Google Docs — giving him a uniquely deep intuition for what it means to build truly collaborative developer tools at scale.Why Agents are Driving Software Development to the Cloud // MLOps Podcast #371 with Zach Lloyd, CEO of WarpWhat we cover:🏗️ Why agents belong in the cloud, not local sandboxes — Zach breaks down why the "set up a local dev box for your agent" approach is fundamentally flawed and what cloud-native agent execution actually looks like in practice.🚀 GitHub is losing collaborative code review — One of the episode's sharpest takes: the hero features of GitHub, like collaborative code review, are migrating into agent workbenches. Zach explains why this shift is structural, not cyclical.📱 "Just-in-time apps" are replacing SaaS — The era of long-lived, learn-to-use-it software may be ending. Zach argues that agents will generate ephemeral, purpose-built interfaces on demand — and why most current app categories are at risk.🤖 Introducing Oz — Warp's cloud orchestration platform — A first look at how Oz works, how Demetrios is already using it to automate podcast production, and what multi-agent orchestration looks like in a real team environment.👁️ Agent observability and why it matters — Debugging, compliance, context management, and handoff/steering: Zach outlines the three pillars every engineering team needs before trusting agents with production work.🔐 Agent chaos is real — access control for AI — Why giving agents too much context is just as dangerous as giving them too little, and how Warp thinks about scoped agent permissions as you scale.📦 SaaS for agents will look nothing like SaaS for humans — The 25-year investment in human-friendly UI is irrelevant for agents. Zach explains what the new infrastructure layer for AI workers will actually need.⚡ Open-weight models will commoditize the coding agent space — With Nvidia investing $2B in open-weight models, Zach believes the current cost advantage that frontier labs hold is temporary — and how Warp is positioning for that world.🧩 Multi-agent orchestration patterns — Parallel agents, agent-to-agent handoffs, and why there's no single "right" pattern yet. Warp's Oz platform is being built for flexibility, not prescription.This episode is essential for engineering leaders, platform engineers, and any developer trying to understand where their daily workflow is headed in the next 18 months.🔗 Links & Resources:Warp: https://www.warp.devWarp Oz platform: https://oz.devZach Lloyd on X/Twitter: https://x.com/zachlloydMLOps Community: https://mlops.communityMLOps Community Slack: https://go.mlops.community/slack⏱️ Timestamps [00:00] Agentic Coding Review Shift[00:29] Warp Collaboration vs Sandboxes[05:22] Continuous Co-Creation in Teams[07:00] Hyperbolics GPU Cloud[07:56] Skill Governance Framework[14:41] Agents vs Browsers Analogy[21:31] PR Provenance in Warp[27:58] Agent System Commandments[37:44] Harness vs ADE[42:03] Adversarial Review Technique[45:26] GitHub Limitations for Agents[49:07] MLflow's GenAI[50:06] Wrap up

This episode is brought to you by the MLflow team. Check out more information at MLflow.org.Mihail Eric is Head of AI at Monaco and Adjunct Lecturer at Stanford University, where he teaches CS146S: "The Modern Software Developer" — the first course in the world dedicated to how AI is transforming every stage of the software development lifecycle. With 12+ years building production AI systems at Amazon Alexa, Storia AI (YC S24), and early-stage startups, Mihail has one of the most grounded, practitioner-level takes on what it actually means to be a software engineer in 2026.The Modern Software Engineer // MLOps Podcast #370 with Mihail Eric, Head of AI at Monaco🧠 What the modern software engineer actually looks like — why the job description has fundamentally shifted from writing code to designing systems and directing agents⚙️ Agents require more thinking, not less — why the engineers getting the most out of coding agents are the ones who invest the most upfront in architecture, planning, and codebase structure🎓 Inside Stanford's "Modern Software Developer" course — what Mihail teaches in the first CS course in the world focused entirely on AI-transformed software development🏗️ From writing code to designing systems — how the best developers are repositioning themselves as architects of agentic workflows rather than line-by-line coders🔁 The Build System: how to run agents at scale — practical lessons from building multi-agent pipelines, parallel subagent batches, and automated retrospectives📉 What junior engineers should actually focus on — the skills that remain irreplaceable and the paths that still produce strong software engineers in an AI-first world🚀 Building Monaco's AI-native revenue engine — what it's like building AI infrastructure for a fast-moving $35M-funded startup disrupting enterprise CRM🎯 How to ace AI engineering interviews — Mihail's framework for demonstrating real AI engineering competence beyond prompt engineering basics. Essential watching for software engineers, ML practitioners, and engineering managers who want an honest, practitioner-level view of where the profession is going — from someone who's both teaching it at Stanford and building it in production.🔗 Links & ResourcesMihail Eric on LinkedIn: https://www.linkedin.com/in/mihaileric/Mihail's website: https://www.mihaileric.comStanford course "The Modern Software Developer": https://themodernsoftware.dev/Maven course — AI Software Development: From First Prompt to Production Code: https://maven.com/the-modern-software-developer/ai-courseFree AI Engineer interview prep course: https://course.aiengineermastery.com/Monaco (AI-native revenue engine): https://monaco.comMLOps.community Slack: https://go.mlops.community/slack⏱️ Timestamps 00:00 Intro — Mihail Eric & Monaco04:00 What has actually changed for software engineers in 202609:00 Inside Stanford's "Modern Software Developer" course15:00 Why agents require more human thinking, not less21:00 From writing code to designing systems — the architect mindset27:00 The Build System: running agents at scale in production33:00 What junior engineers should focus on right now39:00 Building AI infrastructure at Monaco44:00 How to demonstrate real AI engineering competence49:00 Skills that will remain irreplaceable52:00 Rapid fire/closing thoughts

Maher Hanafi is an engineering leader who went from zero AI experience to self-hosting LLMs at enterprise scale — managing GPU costs, optimizing inference with TensorRT LLM, and building an AI platform for HR tech. In this conversation, he breaks down exactly how his team cut latency by 70%, reduced GPU spend through counterintuitive scaling strategies, and navigated the messy reality of taking AI from proof-of-concept to production.How We Cut LLM Latency 70% With TensorRT in Production // MLOps Podcast #369 with Maher Hanafi, SVP of Engineering at Betterworks Key topics covered:The AI Iceberg — Why the invisible work behind AI (performance, latency, throughput, cost, accuracy) is harder than building the features themselvesGPU Cost Optimization — How upgrading to more expensive GPUs actually saved money by reducing total runtime hoursTensorRT LLM Deep Dive — Rewiring neural networks to match GPU architecture for 50-70% latency reductionCold Start Solutions — Using AWS FSx, baking models into container images, and cutting minutes off spin-up timesKV Cache & In-Flight Batching — Why using one model per GPU with maximum KV cache beats cramming multiple models togetherScheduled & Dynamic Scaling — Pattern-based scaling for HR tech workloads (nights, weekends, end-of-quarter spikes)Verticalized AI Platform — Building horizontal AI infrastructure that serves multiple HR product verticalsAI Engineering Lab — How junior vs. senior engineers adopted AI coding tools differently, and the cultural shift that followedAgentic Coding in Practice — Navigating AI coding agent costs, quality control, and redefining the SDLCChinese Models & Compliance — Why enterprise customers block DeepSeek/Qwen and the geopolitics of model training dataThis episode is for engineering leaders building AI in production, MLOps engineers optimizing GPU infrastructure, and anyone navigating the gap between AI demos and enterprise-scale deployment.Links & Resources:TensorRT LLM: https://github.com/NVIDIA/TensorRT-LLMNVIDIA Run: ai Model Streamer (cold start optimization): https://developer.nvidia.com/blog/reducing-cold-start-latency-for-llm-inference-with-nvidia-runai-model-streamer/vLLM vs TensorRT-LLM comparison: https://northflank.com/blog/vllm-vs-tensorrt-llm-and-how-to-run-themTimestamps: [00:00] Optimizing GPU Usage and Latency[00:21] Learning AI as Leadership[04:34] AI Cost Centers[13:56] Throughput and Infrastructure Efficiency[18:10] Scaling and Unit Economics[24:14] Championing AI ROI[36:11] Queue to Value Engine[41:30] Failed Product Features[46:12] Agentic Engineering Costs[58:49] AI Self-Hosting in Engineering[1:04:40] Wrap up

Rob Ennals is the creator of Broomy, an open-source IDE designed for working effectively with many agents in parallel. He previously worked at Meta, Quora, Google Search, and Intel Research. He has a PhD in Computer Science from the University of Cambridge.Getting Humans Out of the Way: How to Work with Teams of Agents // MLOps Podcast #368 with Rob Ennals, the Creator of Broomy Join the Community: https://go.mlops.community/YTJoinInGet the newsletter: https://go.mlops.community/YTNewsletterMLOps GPU Guide: https://go.mlops.community/gpuguide// AbstractMost people cripple coding agents by micromanaging them—reviewing every step and becoming the bottleneck.The shift isn’t to better supervise agents, but to design systems where they work well on their own: parallelized, self-validating, and guided by strong processes.Done right, you don’t lose control—you gain leverage. Like paving roads for cars, the real unlock is reshaping the environment so AI can move fast.// BioRob Ennals is the creator of Broomy, an open-source IDE designed for working effectively with many agents in parallel. He previously worked at Meta, Quora, Google Search, and Intel Research. He has a PhD in Computer Science from the University of Cambridge.// Related LinksWebsite: https://robennals.org/https://broomy.org/https://learnai.robennals.org/ (not yet announced, but should be by the time of the podcast)~~~~~~~~ ✌️Connect With Us ✌️ ~~~~~~~Catch all episodes, blogs, newsletters, and more: https://go.mlops.community/TYExploreJoin our Slack community [https://go.mlops.community/slack]Follow us on X/Twitter [@mlopscommunity](https://x.com/mlopscommunity) or [LinkedIn](https://go.mlops.community/linkedin)] Sign up for the next meetup: [https://go.mlops.community/register]MLOps Swag/Merch: [https://shop.mlops.community/]Connect with Demetrios on LinkedIn: /dpbrinkmConnect with Rob on LinkedIn: /robennals/Timestamps:[00:00] Agent Optimization Strategies[00:21] Visual Regression Explanation[05:35] Automated QA for Videos[13:05] Verification System Design[19:48] Agent Selection Strategies[30:48] Parallel Agent Management[35:30] Containerization and Cost Estimation[42:48] Shifting to Agent Orchestration[50:10] Wrap up

Kashish Mittal is a Staff Software Engineer at Uber, working on large-scale distributed systems and core backend infrastructure.Fixing GPU Starvation in Large-Scale Distributed Training // MLOps Podcast #367 with Kashish Mittal, Staff Software Engineer at Uber Join the Community: https://go.mlops.community/YTJoinInGet the newsletter: https://go.mlops.community/YTNewsletterMLOps GPU Guide: https://go.mlops.community/gpuguide// Abstract Kashish zooms out to discuss a universal industry pattern: how infrastructure—specifically data loading—is almost always the hidden constraint for ML scaling.The conversation dives deep into a recent architectural war story. Kashish walks through the full-stack profiling and detective work required to solve a massive GPU starvation bottleneck. By redesigning the Petastorm caching layer to bypass CPU transformation walls and uncovering hidden distributed race conditions, his team boosted GPU utilization to 60%+ and cut training time by 80%. Kashish also shares his philosophy on the fundamental trade-offs between latency and efficiency in GPU serving.// BioKashish Mittal is a Staff Software Engineer at Uber, where he architects the hyperscale machine learning infrastructure that powers Uber’s core mobility and delivery marketplaces. Prior to Uber, Kashish spent nearly a decade at Google building highly scalable, low-latency distributed ML systems for flagship products, including YouTube Ads and Core Search Ranking. His engineering expertise lies at the intersection of distributed systems and AI—specifically focusing on large-scale data processing, eliminating critical I/O bottlenecks, and maximizing GPU efficiency for petabyte-scale training pipelines. When he isn't hunting down distributed race conditions, he is a passionate advocate for open-source architecture and building reproducible, high-throughput ML systems.// Related LinksWebsite: https://www.uber.com/Getting Humans Out of the Way: How to Work with Teams of Agents // MLOps Podcast #368 with Rob Ennals, the Creator of Broomy: https://www.youtube.com/watch?v=ie1M8p-SVfM~~~~~~~~ ✌️Connect With Us ✌️ ~~~~~~~Catch all episodes, blogs, newsletters, and more: https://go.mlops.community/TYExploreJoin our Slack community [https://go.mlops.community/slack]Follow us on X/Twitter [@mlopscommunity](https://x.com/mlopscommunity) or [LinkedIn](https://go.mlops.community/linkedin)] Sign up for the next meetup: [https://go.mlops.community/register]MLOps Swag/Merch: [https://shop.mlops.community/]Connect with Demetrios on LinkedIn: /dpbrinkmConnect with Kashish on LinkedIn: /kashishmittal/Timestamps:[00:00] Local dataset caching[00:30] Engineers Evolving Roles[04:44] GPU Resource Management[10:21] GPU Utilization Issues[21:49] More GPU War Stories[32:12] Model Serving Issues[39:58] Reflective Learning in Coding[43:23] Workflow and Reflective Skills[52:30] Wrap up

Jens Bodal is a Senior Software Engineer II working independently, focusing on backend systems, software architecture, and building scalable solutions across client projects.This One Shift Makes Developers Obsolete // MLOps Podcast #366 with Jens Bodal, Senior Software Engineer II, Independent Join the Community: https://go.mlops.community/YTJoinInGet the newsletter: https://go.mlops.community/YTNewsletterMLOps GPU Guide: https://go.mlops.community/gpuguide// Abstract AI agents are shifting the role of developers from writing code to defining intent. This conversation explores why specs are becoming more important than implementation, what breaks in real-world systems, and how engineering teams need to rethink workflows in an agent-driven world.// BioJens Bodal is a senior software engineer based in Edmonds, Washington, with nine years of experience building developer tooling, internal platforms, and web infrastructure. He spent seven years as an SDE II at Amazon, working on teams including Amazon Games Studio and the AWS Events Management Platform. His work has focused on developer tooling, CI/CD systems, testing infrastructure, and improving the developer experience for teams operating production services. He is particularly interested in developer experience and the growing ecosystem of local tools that help engineers build and run AI systems on infrastructure they control.// Related LinksWebsite: https://bodal.devhttps://github.com/jensbodalhttps://www.youtube.com/watch?v=Yp7LYdbOuwE~~~~~~~~ ✌️Connect With Us ✌️ ~~~~~~~Catch all episodes, blogs, newsletters, and more: https://go.mlops.community/TYExploreJoin our Slack community [https://go.mlops.community/slack]Follow us on X/Twitter [@mlopscommunity](https://x.com/mlopscommunity) or [LinkedIn](https://go.mlops.community/linkedin)] Sign up for the next meetup: [https://go.mlops.community/register]MLOps Swag/Merch: [https://shop.mlops.community/]Connect with Demetrios on LinkedIn: /dpbrinkmConnect with Jens on LinkedIn: /jensbodalTimestamps:[00:00] Specification vs Code[00:25] Conference Realizations and Insights[09:01] Agents and Orchestration Insights[10:39] Coding Agents and Talent[18:10] Sub-agent Design Concepts[25:18] Evaling on Vibes[33:23] Walled Garden and Proxies [41:48] Spec-Driven Development Limitations[46:56] Code Ownership vs Authorship[50:49] Engineering Ownership and PMs[53:47] Skill Creation and Iteration[58:40] Wrap up

Databricks Roundtable episode: Operationalizing AI Agents: From Experimentation to Production. Join the Community: https://go.mlops.community/YTJoinInGet the newsletter: https://go.mlops.community/YTNewsletterMLOps GPU Guide: https://go.mlops.community/gpuguideBig shout-out to Databricks for the collaboration!// AbstractThis panel discusses the real-world challenges of deploying AI agents at scale. The conversation explores technical and operational barriers that slow production adoption, including reliability, cost, governance, and security.The panelists also examine how LLMOps, AIOps, and AgentOps differ from traditional MLOps, and why new approaches are required for generative and agent-based systems. Finally, experts define success criteria for GenAI frameworks, with a focus on robust evaluation, observability, and continuous monitoring across development and staging environments.// BioSamraj MoorjaniSamraj is a software engineer working on the Agent Quality team. Previously, Samraj worked at Meta on ads/product classification research and AppLovin on MLOps. Samraj graduated with a BS+MS in Computer Science from UIUC, advised by Professor Hari Sundaram, where he worked on controllable natural language generation to produce appealing, interpretable science to combat the spread of misinformation. He also worked with Professor Wen-mei Hwu on accelerating LLM inference through extreme sparsification.Apurva MisraApurva is an AI Consultant at Sentick, focusing on assisting startups with their AI strategy and building solutions. She leverages her extensive experience in machine learning and a Master's degree from the University of Waterloo, where her research bridged driving and machine learning, to offer valuable insights. Apurva's keen interest in the startup world fuels her passion for helping emerging companies incorporate AI effectively. In her free time, she is learning Spanish, and she also enjoys exploring hidden gem eateries, always eager to hear about new favourite spots!Ben EpsteinBen was the machine learning lead for Splice Machine, leading the development of their MLOps platform and Feature Store. He is now the Co-founder and CTO at GrottoAI, focused on supercharging multifamily teams and reducing vacancy loss with AI-powered guidance for leasing and renewals. Ben also works as an adjunct professor at Washington University in St. Louis, teaching concepts in cloud computing and big data analytics.Hosted by Adam Becker// Related LinksWebsite: https://www.databricks.com/https://mlflow.org/~~~~~~~~ ✌️Connect With Us ✌️ ~~~~~~~Catch all episodes, blogs, newsletters, and more: https://go.mlops.community/TYExploreJoin our Slack community [https://go.mlops.community/slack]Follow us on X/Twitter [@mlopscommunity](https://x.com/mlopscommunity) or [LinkedIn](https://go.mlops.community/linkedin)] Sign up for the next meetup: [https://go.mlops.community/register]MLOps Swag/Merch: [https://shop.mlops.community/]Connect with Demetrios on LinkedIn: /dpbrinkmConnect with Samraj on LinkedIn: /samrajmoorjani/Connect with Apurva on LinkedIn: /apurva-misra/Connect with Ben on LinkedIn: /ben-epstein/Connect with Adam on LinkedIn: /adamissimo/Timestamps:[00:00] Introduction[02:30] AI Agents in Operations[04:36] AI Strategy Consulting[05:30] Agent Quality Focus[06:17] AI Agent Expectations[11:44] AI Use Cases Evolution[15:25] Agent Expectations Adjustment[17:41] Agent Quality Monitoring[23:22] Trust in GenAI Systems[33:33] Data Prep vs Product Thinking[40:27] Quality Systems Distinction[44:54] Q & A[1:00:57] Wrap up

Lorenzo Moriondo is a Technical Lead for AI at tuned.org.uk, working on AI agent protocols, graph-based search, and production-grade LLM systems.arrowspace: Vector Spaces and Graph Wiring // MLOps Podcast #365 with Lorenzo Moriondo, AI Research and Product EngineerJoin the Community: https://go.mlops.community/YTJoinInGet the newsletter: https://go.mlops.community/YTNewsletterMLOps GPU Guide: https://go.mlops.community/gpuguide// Abstract Meet arrowspace — an open-source library for curating and understanding LLM datasets across the entire lifecycle, from pre-training to inference. Instead of treating embeddings as static vectors, arrowspace turns them into graphs (“graph wiring”) so you can explore structure, not just similarity. That unlocks smarter RAG search (beyond basic semantic matching), dataset fingerprinting, and deeper insights into how different datasets behave.You can compare datasets, predict how changes will affect performance, detect drift early, and even safely mix data sources while measuring outcomes.In short: arrowspace helps you see your data — and make better decisions because of it.// BioWith over a decade of experience in software and data engineering across startups and early-stage projects, Lorenzo has recently turned his focus to the AI-assisted movement to automate software and data operations. He has contributed to and founded projects within various open-source communities, including work with Summer of Code, where he focused on the Semantic Web and REST APIs.A strong enthusiast of Python and Rust, he develops tools centered around LLMs and agentic systems. He is a maintainer of the SmartCore ML library, as well as the creator of Arrowspace and the Topological Transformer.// Related LinksWebsite: https://www.tuned.org.uk~~~~~~~~ ✌️Connect With Us ✌️ ~~~~~~~Catch all episodes, blogs, newsletters, and more: https://go.mlops.community/TYExploreJoin our Slack community [https://go.mlops.community/slack]Follow us on X/Twitter [@mlopscommunity](https://x.com/mlopscommunity) or [LinkedIn](https://go.mlops.community/linkedin)] Sign up for the next meetup: [https://go.mlops.community/register]MLOps Swag/Merch: [https://shop.mlops.community/]Connect with Demetrios on LinkedIn: /dpbrinkmConnect with Chris on LinkedIn: /lorenzomoriondoTimestamps:[00:00] Graph Wiring for ML[00:32] RAG and Vector Similarity[08:58] Geometric Search Trade-offs[13:12] Vector DB Algorithm Integration[21:32] Feature-Based Retrieval Shift[26:04] Epiplexity and Embeddings[31:26] Epiplexity and Embedding Structure[40:15] Training vs Post-hoc Models[47:16] Discovery-Driven Development[51:22] Updating Mental Models[53:00] Vector Search vs Agents[55:30] Wrap up

Donné Stevenson is a Machine Learning Engineer at Prosus, working on scalable ML infrastructure and productionizing GenAI systems across portfolio companies.Pedro Chaves is a Data Science Manager at OLX Group, working on GenAI-powered search, personalization, and large-scale marketplace recommendations.Join the Community: https://go.mlops.community/YTJoinInGet the newsletter: https://go.mlops.community/YTNewsletterMLOps GPU Guide: https://go.mlops.community/gpuguide// AbstractMarketplaces are about to get smarter.Agents that find your perfect house, negotiate the best deals, and even talk to other agents on your behalf.Less tedious searching. Less back-and-forth. More time for what matters.Pedro Chaves and Donné Stevenson discuss the future of buying and selling cars, homes, and everything in between - and what it'll take to get there.// BioDonné StevensonFocused on building AI-powered products that give companies the tools and expertise needed to harness the power of AI in their respective fields.Pedro ChavesPedro is a Data Science Manager at OLX Group, where he leads teams building machine learning solutions to improve marketplace performance, pricing, and user experience at scale.// Related LinksWebsite: https://www.prosus.com/Website: https://www.olxgroup.com/~~~~~~~~ ✌️Connect With Us ✌️ ~~~~~~~Catch all episodes, blogs, newsletters, and more: https://go.mlops.community/TYExploreJoin our Slack community [https://go.mlops.community/slack]Follow us on X/Twitter [@mlopscommunity](https://x.com/mlopscommunity) or [LinkedIn](https://go.mlops.community/linkedin)] Sign up for the next meetup: [https://go.mlops.community/register]MLOps Swag/Merch: [https://shop.mlops.community/]MLOps GPU Guide: https://go.mlops.community/gpuguideTimestamps:[00:00] OLX: Disrupting Buyer-Seller Experiences[03:33] Redefining the Home-Buying Experience[07:40] User Feedback and Iterative Rollouts[11:25] Beyond Chat: Redefining Agent Use[14:03] User Trust and Education Challenges[16:47] Learning Curve for Automoto[20:05] Interactive Decision-Making with AI[24:47] Agents Simplify Buyer-Seller Search[28:14] Garage Sale Treasure Hunting[33:43] Agent Discovery Layer Needed[34:53] Agents Relying on Agents[39:48] Reducing Friction in Selling Stuff[41:39] Extracting Buyer Intent Systematically[44:49] Optimizing Delivery with Lockers[50:10] Generative AI Commerce Strategies[51:03] Improving Chat Interaction Layer

Johann Schleier-Smith is the Technical Lead for AI at Temporal Technologies, working on reliable infrastructure for production AI systems and long-running agent workflows. Durable Execution and Modern Distributed Systems, Johann Schleier-Smith // MLOps Podcast #364Join the Community: https://go.mlops.community/YTJoinInGet the newsletter: https://go.mlops.community/YTNewsletterMLOps Merch: https://shop.mlops.community/Big shoutout to ⁨ @Temporalio  for the support, and to  @trychroma  for hosting us in their recording studio// AbstractA new paradigm is emerging for building applications that process large volumes of data, run for long periods of time, and interact with their environment. It’s called Durable Execution and is replacing traditional data pipelines with a more flexible approach. Durable Execution makes regular code reliable and scalable.In the past, reliability and scalability have come from restricted programming models, like SQL or MapReduce, but with Durable Execution, this is no longer the case. We can now see data pipelines that include document processing workflows, deep research with LLMs, and other complex and LLM-driven agentic patterns expressed at scale with regular Python programs.In this session, we describe Durable Execution and explain how it fits in with agents and LLMs to enable a new class of machine learning applications.// Related Linkshttps://t.mp/hello?utm_source=podcast&utm_medium=sponsorship&utm_campaign=podcast-2026-03-13-mlops&utm_content=mlops-johannhttps://t.mp/vibe?utm_source=podcast&utm_medium=sponsorship&utm_campaign=podcast-2026-03-13-mlops&utm_content=mlops-johannhttps://t.mp/career?utm_source=podcast&utm_medium=sponsorship&utm_campaign=podcast-2026-03-13-mlops&utm_content=mlops-johann ~~~~~~~~ ✌️Connect With Us ✌️ ~~~~~~~Catch all episodes, blogs, newsletters, and more: https://go.mlops.community/TYExploreJoin our Slack community [https://go.mlops.community/slack]Follow us on X/Twitter [@mlopscommunity](https://x.com/mlopscommunity) or [LinkedIn](https://go.mlops.community/linkedin)] Sign up for the next meetup: [https://go.mlops.community/register]MLOps Swag/Merch: [https://shop.mlops.community/]Connect with Demetrios on LinkedIn: /dpbrinkmConnect with Johann on LinkedIn: /jssmith/

March 3rd, Computer History Museum CODING AGENTS CONFERENCE, come join us while there are still tickets left.https://luma.com/codingagentsChris Fregly is currently focused on building and scaling high-performance AI systems, writing and teaching about AI infrastructure, helping organizations adopt generative AI and performance engineering principles on AWS, and fostering large developer communities around these topics.Performance Optimization and Software/Hardware Co-design across PyTorch, CUDA, and NVIDIA GPUs // MLOps Podcast #363 with Chris Fregly, Founder, AI Performance Engineer, and InvestorJoin the Community: https://go.mlops.community/YTJoinInGet the newsletter: https://go.mlops.community/YTNewsletterMLOps GPU Guide: https://go.mlops.community/gpuguide// AbstractIn today’s era of massive generative models, it's important to understand the full scope of AI systems' performance engineering. This talk discusses the new O'Reilly book, AI Systems Performance Engineering, and the accompanying GitHub repo (https://github.com/cfregly/ai-performance-engineering). This talk provides engineers, researchers, and developers with a set of actionable optimization strategies. You'll learn techniques to co-design and co-optimize hardware, software, and algorithms to build resilient, scalable, and cost-effective AI systems for both training and inference. // BioChris Fregly is an AI performance engineer and startup founder with experience at AWS, Databricks, and Netflix. He's the author of three (3) O'Reilly books, including Data Science on AWS (2021), Generative AI on AWS (2023), and AI Systems Performance Engineering (2025). He also runs the global AI Performance Engineering meetup and speaks at many AI-related conferences, including Nvidia GTC, ODSC, Big Data London, and more.// Related LinksAI Systems Performance Engineering: Optimizing Model Training and Inference Workloads with GPUs, CUDA, and PyTorch 1st Edition by Chris Fregly: https://www.amazon.com/Systems-Performance-Engineering-Optimizing-Algorithms/dp/B0F47689K8/Coding Agents Conference: https://luma.com/codingagents~~~~~~~~ ✌️Connect With Us ✌️ ~~~~~~~Catch all episodes, blogs, newsletters, and more: https://go.mlops.community/TYExploreJoin our Slack community [https://go.mlops.community/slack]Follow us on X/Twitter [@mlopscommunity](https://x.com/mlopscommunity) or [LinkedIn](https://go.mlops.community/linkedin)] Sign up for the next meetup: [https://go.mlops.community/register]MLOps Swag/Merch: [https://shop.mlops.community/]Connect with Demetrios on LinkedIn: /dpbrinkmConnect with Chris on LinkedIn: /cfreglyTimestamps:[00:00] SageMaker HyperPod Resilience[00:27] Book Creation and Software Engineering[04:57] Software Engineers and Maintenance[11:49] AI Systems Performance Engineering[22:03] Cognitive Biases and Optimization / "Mechanical Sympathy"[29:36] GPU Rack-Scale Architecture[33:58] Data Center Reliability Issues[43:52] AI Compute Platforms[49:05] Hardware vs Ecosystem Choice[1:00:05] Claude vs Codex vs Gemini[1:14:53] Kernel Budget Allocation[1:18:49] Steerable Reasoning Challenges[1:24:18] Data Chain Value Awareness

Roundtable CAST AI episode: Serving LLMs in Production: Performance, Cost & Scale. Join the Community: https://go.mlops.community/YTJoinInGet the newsletter: https://go.mlops.community/YTNewsletterMLOps GPU Guide: https://go.mlops.community/gpuguide// AbstractExperimenting with LLMs is easy. Running them reliably and cost-effectively in production is where things break. Most AI teams never make it past demos and proofs of concept. A smaller group is pushing real workloads to production—and running into very real challenges around infrastructure efficiency, runaway cloud costs, and reliability at scale.This session is for engineers and platform teams moving beyond experimentation and building AI systems that actually hold up in production.// BioIoana ApetreiIoana is a Senior Product Manager at CAST AI, leading the AI Enabler product, an AI Gateway platform for cost-effective LLM infrastructure deployment. She brings 12 years of experience building B2C and B2B products reaching over 10 million users. Outside of work, she enjoys assembling puzzles and LEGOs and watching motorsports.Igor ŠušićIgor is a founding Machine Learning Engineer at CAST AI’s AI Enabler, where he focuses on optimizing inference and training at scale. With a strong background in Natural Language Processing (NLP) and Recommender Systems, Igor has been tackling the challenges of large-scale model optimization long before transformers became mainstream. Prior to CAST AI, he worked at industry leaders like Bloomreach and Infobip, where he contributed to the development and deployment of large-scale AI and personalization systems from the early days of the field.// Related LinksWebsite: https://cast.ai/~~~~~~~~ ✌️Connect With Us ✌️ ~~~~~~~Catch all episodes, blogs, newsletters, and more: https://go.mlops.community/TYExploreJoin our Slack community [https://go.mlops.community/slack]Follow us on X/Twitter [@mlopscommunity](https://x.com/mlopscommunity) or [LinkedIn](https://go.mlops.community/linkedin)] Sign up for the next meetup: [https://go.mlops.community/register]MLOps Swag/Merch: [https://shop.mlops.community/]Connect with Demetrios on LinkedIn: /dpbrinkmConnect with Ioana on LinkedIn: /ioanaapetrei/Connect with Igor on LinkedIn: /igor-%C5%A1u%C5%A1i%C4%87/

Rahul Raja is a Staff Software Engineer at LinkedIn, working on large-scale search infrastructure, information retrieval systems, and integrating AI/ML to improve ranking and semantic search experiences.The Future of Information Retrieval: From Dense Vectors to Cognitive Search // MLOps Podcast #362 with Rahul Raja, Staff Software Engineer at LinkedInJoin the Community: https://go.mlops.community/YTJoinInGet the newsletter: https://go.mlops.community/YTNewsletterMLOps GPU Guide: https://go.mlops.community/gpuguide// AbstractInformation Retrieval is evolving from keyword matching to intelligent, vector-based understanding. In this talk, Rahul Raja explores how dense retrieval, vector databases, and hybrid search systems are redefining how modern AI retrieves, ranks, and reasons over information. He discusses how retrieval now powers large language models through Retrieval-Augmented Generation (RAG) and the new MLOps challenges that arise, embedding drift, continuous evaluation, and large-scale vector maintenance.Looking ahead, the session envisions a future of Cognitive Search, where retrieval systems move beyond recall to genuine reasoning, contextual understanding, and multimodal awareness. Listeners will gain insight into how the next generation of retrieval will bridge semantics, scalability, and intelligence, powering everything from search and recommendations to generative AI.// BioRahul is a Staff Engineer at LinkedIn, where he focuses on search and deployment systems at scale. Rahul is a graduate from Carnegie Mellon University and has a strong background in building reliable, high-performance infrastructure. He has led many initiatives to improve search relevance and streamline ML deployment workflows.// Related LinksWebsite: https://www.linkedin.com/Coding Agents Conference: https://luma.com/codingagents~~~~~~~~ ✌️Connect With Us ✌️ ~~~~~~~Catch all episodes, blogs, newsletters, and more: https://go.mlops.community/TYExploreJoin our Slack community [https://go.mlops.community/slack]Follow us on X/Twitter [@mlopscommunity](https://x.com/mlopscommunity) or [LinkedIn](https://go.mlops.community/linkedin)] Sign up for the next meetup: [https://go.mlops.community/register]MLOps Swag/Merch: [https://shop.mlops.community/]Connect with Demetrios on LinkedIn: /dpbrinkmConnect with Rahul on LinkedIn: /rahulraja963/Timestamps:[00:00] Vector Search for Media[00:33] RAG and Search Evolution[04:45] Cognitive vs Semantic Search[08:26] High Value Search Signals[16:43] Scaling with Embeddings[22:37] BM25 Benchmark Bias[29:00] Video Search Use Cases[31:21] Context and Search Tradeoff[35:04] Personal Memory Augmentation[39:03] Future of Cognitive Search[44:51] Access Control in Vectors[49:14] Search Ranking Challenge[54:43] Hard Search Problems Solved[58:29] Freshness vs Cost[1:02:12] Wrap up

Vincent Warmerdam is a Founding Engineer at marimo, working on reinventing Python notebooks as reactive, reproducible, interactive, and Git-friendly environments for data workflows and AI prototyping. He helps build the core marimo notebook platform, pushing its reactive execution model, UI interactivity, and integration with modern development and AI tooling so that notebooks behave like dependable, shareable programs and apps rather than error-prone scratchpads.Join the Community: https://go.mlops.community/YTJoinInGet the newsletter: https://go.mlops.community/YTNewsletterMLOps GPU Guide: https://go.mlops.community/gpuguide// AbstractVincent Warmerdam joins Demetrios fresh off marimo’s acquisition by Weights & Biases—and makes a bold claim: notebooks as we know them are outdated.They talk Molab (GPU-backed, cloud-hosted notebooks), LLMs that don’t just chat but actually fix your SQL and debug your code, and why most data folks are consuming tools instead of experimenting. Vincent argues we should stop treating notebooks like static scratchpads and start treating them like dynamic apps powered by AI.It’s a conversation about rethinking workflows, reclaiming creativity, and not outsourcing your brain to the model.// BioVincent is a senior data professional who worked as an engineer, researcher, team lead, and educator in the past. You might know him from tech talks with an attempt to defend common sense over hype in the data space. He is especially interested in understanding algorithmic systems so that one may prevent failure. As such, he has always had a preference to keep calm and check the dataset before flowing tonnes of tensors. He currently works at marimo, where he spends his time rethinking everything related to Python notebooks.// Related LinksWebsite: https://marimo.io/Coding Agent Conference: https://luma.com/codingagentsHyperbolic GPU Cloud: app.hyperbolic.ai~~~~~~~~ ✌️Connect With Us ✌️ ~~~~~~~Catch all episodes, blogs, newsletters, and more: https://go.mlops.community/TYExploreJoin our Slack community [https://go.mlops.community/slack]Follow us on X/Twitter [@mlopscommunity](https://x.com/mlopscommunity) or [LinkedIn](https://go.mlops.community/linkedin)] Sign up for the next meetup: [https://go.mlops.community/register]MLOps Swag/Merch: [https://shop.mlops.community/]MLOps GPU Guide: https://go.mlops.community/gpuguideConnect with Demetrios on LinkedIn: /dpbrinkmConnect with Vincent on LinkedIn: /vincentwarmerdam/Timestamps:[00:00] Context in Notebooks[00:24] Acquisition and Team Continuity[04:43] Coding Agent Conference Announcement![05:56] Hyperbolic GPU Cloud Ad[06:54] marimo and W&B Synergies[09:31] marimo Cloud Code Support[12:59] Hardest Code to Generate[16:22] Trough of Disillusionment[20:38] Agent Interaction in Notebooks[25:41] Wrap up

Ereli Eran is the Founding Engineer at 7AI, where he’s focused on building and scaling the company’s agentic AI-driven cybersecurity platform — developing autonomous AI agents that triage alerts, investigate threats, enrich security data, and enable end-to-end automated security operations so human teams can focus on higher-value strategic work.Software Engineering in the Age of Coding Agents: Testing, Evals, and Shipping Safely at Scale // MLOps Podcast #361 with Ereli Eran, Founding Engineer at 7AIJoin the Community: https://go.mlops.community/YTJoinInGet the newsletter: https://go.mlops.community/YTNewsletterMLOps GPU Guide: https://go.mlops.community/gpuguide// AbstractA conversation on how AI coding agents are changing the way we build and operate production systems. We explore the practical boundaries between agentic and deterministic code, strategies for shared responsibility across models, engineering teams, and customers, and how to evaluate agent performance at scale. Topics include production quality gates, safety and cost tradeoffs, managing long-tail failures, and deployment patterns that let you ship agents with confidence.// BioEreli Eran is a founding engineer at 7AI, where he builds agentic AI systems for security operations and the production infrastructure that powers them. His work spans the full stack - from designing experiment frameworks for LLM-based alert investigation to architecting secure multi-tenant systems with proper authentication boundaries. Previously, he worked in data science and software engineering roles at Stripe, VMware Carbon Black, and was an early employee of Ravelin and Normalyze.// Related LinksWebsite: https://7ai.com/Coding Agents Conference: https://luma.com/codingagents~~~~~~~~ ✌️Connect With Us ✌️ ~~~~~~~Catch all episodes, blogs, newsletters, and more: https://go.mlops.community/TYExploreJoin our Slack community [https://go.mlops.community/slack]Follow us on X/Twitter [@mlopscommunity](https://x.com/mlopscommunity) or [LinkedIn](https://go.mlops.community/linkedin)] Sign up for the next meetup: [https://go.mlops.community/register]MLOps Swag/Merch: [https://shop.mlops.community/]Connect with Demetrios on LinkedIn: /dpbrinkmConnect with Ereli on LinkedIn: /erelieran/Timestamps:[00:00] Language Sensitivity in Reasoning[00:25] Value of Claude Code[01:54] AI in Security Workflows[06:21] Agentic Systems Failures[12:50] Progressive Disclosure in Voice Agents[16:39] LLM vs Classic ML[19:44] Hybrid Approach to Fraud[25:58] Debugging with User Feedback[33:52] Prompts as Code[42:07] LLM Security Workflow[45:10] Shared Memory in Security[49:11] Common Agent Failure Modes[53:34] Wrap up

Nick Gillian is the Co-Founder and CTO at Archetype AI, working on physical AI foundation models that understand and reason over real-world sensor data.Physical AI: Teaching Machines to Understand the Real World // MLOps Podcast #360 with Nick Gillian, Co-Founder and CTO of Archetype AIJoin the Community: https://go.mlops.community/YTJoinInGet the newsletter: https://go.mlops.community/YTNewsletterMLOps GPU Guide: https://go.mlops.community/gpuguide/ AbstractAs AI moves beyond the cloud and simulation, the next frontier is Physical AI: systems that can perceive, understand, and act within real-world environments in real time. In this conversation, Nick Gillian, Co-Founder and CTO of Archetype AI, explores what it actually takes to turn raw sensor and video data into reliable, deployable intelligence.Drawing on his experience building Google’s Soli and Jacquard and now leading development of Newton, a foundational model for Physical AI, Nick discusses how real-time physical understanding changes what’s possible across safety monitoring, infrastructure, and human–machine interaction. He’ll share lessons learned translating advanced research into products that operate safely in dynamic environments, and why many organizations underestimate the challenges and opportunities of AI in the physical world.// BioNick Gillian, Ph.D., is Co-Founder and CTO of Archetype AI with over 15 years of experience turning advanced AI and interaction research into real-world products. At Archetype, he leads the AI and engineering teams behind Newton—a first-of-its-kind Physical AI foundational model that can perceive, understand, and reason about the physical world. Before co-founding Archetype, Nick was a Senior Staff Machine Learning Engineer at Google and a researcher at MIT, where he developed AI and ML methods for real-time sensor understanding. At Google’s Advanced Technology and Projects group, he led machine learning research that powered breakthrough products like Soli radar and Jacquard, and helped advance sensing algorithms across Pixel, Nest, and wearable devices.// Related LinksWebsite: https://www.archetypeai.io/https://www.archetypeai.io/blog/timefusion-newton https://www.nature.com/articles/s41598-023-44714-2https://www.youtube.com/watch?v=Pow4utY9teU https://www.youtube.com/watch?v=uE0jjdzwe9w https://arxiv.org/abs/2410.14724 Coding Agents Conference: https://luma.com/codingagents~~~~~~~~ ✌️Connect With Us ✌️ ~~~~~~~Catch all episodes, blogs, newsletters, and more: https://go.mlops.community/TYExploreJoin our Slack community [https://go.mlops.community/slack]Follow us on X/Twitter [@mlopscommunity](https://x.com/mlopscommunity) or [LinkedIn](https://go.mlops.community/linkedin)] Sign up for the next meetup: [https://go.mlops.community/register]MLOps Swag/Merch: [https://shop.mlops.community/]Connect with Demetrios on LinkedIn: /dpbrinkmConnect with Nick on LinkedIn: /nick-gillian-b27b1094/Timestamps:[00:00] Physical Agent Framework[00:56] Physical AI Clarification[06:53] Building a Repair Model[12:41] World Models and LLMs[17:17] Data Weighting Strategies[24:19] Data Diversity vs Quantity[38:30] R&D and Product Creation[41:22] Construction Site Data Shipping[50:33] Wrap up

Kris Beevers is the CEO at NetBox Labs, working on turning NetBox into the system of record and automation backbone for modern and AI-driven infrastructure.Speed and Scale: How Today's AI Datacenters Are Operating Through Hypergrowth // MLOps Podcast #359 with Kris Beevers, CEO of NetBox LabsJoin the Community: https://go.mlops.community/YTJoinInGet the newsletter: https://go.mlops.community/YTNewsletterMLOps GPU Guide: https://go.mlops.community/gpuguide// AbstractHundreds of neocloud operators and "AI Factory" builders have emerged to serve the insatiable demand for AI infrastructure. These teams are compressing the design, build, deploy, operate, scale cycle of their infrastructures down to months, while managing massive footprints with lean teams. How? By applying modern intent-driven infrastructure automation principles to greenfield deployments. We'll explore how these teams carry design intent through to production, and how operating and automating around consistent infrastructure data is compressing "time to first train".// BioKris Beevers is the Co-founder and CEO of NetBox Labs. NetBox is used by nearly every Neocloud and AI datacenter to manage their networks and infrastructure. Kris is an engineer at heart and by background, and loves the leverage infrastructure innovation creates to accelerate technology and empower engineers to do their best work. A serial entrepreneur, Kris has founded and helped lead multiple other successful businesses in the internet and network infrastructure. Most recently, he co-founded and led NS1, which was acquired by IBM in 2023. He holds a Ph.D. in Computer Science from Rensselaer Polytechnic Institute and is based in New Jersey.// Related LinksWebsite: https://netboxlabs.com/Coding Agents Conference: https://luma.com/codingagents~~~~~~~~ ✌️Connect With Us ✌️ ~~~~~~~Catch all episodes, blogs, newsletters, and more: https://go.mlops.community/TYExploreJoin our Slack community [https://go.mlops.community/slack]Follow us on X/Twitter [@mlopscommunity](https://x.com/mlopscommunity) or [LinkedIn](https://go.mlops.community/linkedin)] Sign up for the next meetup: [https://go.mlops.community/register]MLOps Swag/Merch: [https://shop.mlops.community/]Connect with Demetrios on LinkedIn: /dpbrinkmConnect with Kris on LinkedIn: /beevek/Timestamps:[00:00] Observability and Delta Analysis[00:26] New World Exploration[04:06] Bottlenecks in AI Infrastructure[13:37] Data Center Optimization Challenges[19:58] Tech Stack Breakdown[25:26] Data Center Design Principles[31:32] Constraints and Automation in Design[40:00] Complexity in Data Centers[45:02] GPU Cloud Landscape[50:24] Data Centers in Containers[57:45] Observability Beyond Software[1:04:43] Tighter Integrations vs NetBox[1:06:47] Wrap up

Mike Oaten is the Founder and CEO of TIKOS, working on building AI assurance, explainability, and trustworthy AI infrastructure, helping organizations test, monitor, and govern AI models and systems to make them transparent, fair, robust, and compliant with emerging regulations.Cracking the Black Box: Real-Time Neuron Monitoring & Causality Traces // MLOps Podcast #358 with Mike Oaten, Founder and CEO of TIKOSJoin the Community: https://go.mlops.community/YTJoinInGet the newsletter: https://go.mlops.community/YTNewsletter// AbstractAs AI models move into high-stakes environments like Defence and Financial Services, standard input/output testing, evals, and monitoring are becoming dangerously insufficient. To achieve true compliance, MLOps teams need to access and analyse the internal reasoning of their models to achieve compliance with the EU AI Act, NIST AI RMF, and other requirements.In this session, Mike introduces the company's patent-pending AI assurance technology that moves beyond statistical proxies. He will break down the architecture of the Synapses Logger, a patent-pending technology that embeds directly into the neural activation flow to capture weights, activations, and activation paths in real-time.// BioMike Oaten serves as the CEO of TIKOS, leading the company’s mission to progress trustworthy AI through unique, high-performance AI model assurance technology. A seasoned technical and data entrepreneur, Mike brings experience from successfully co-founding and exiting two previous data science startups: Riskopy Inc. (acquired by Nasdaq-listed Coupa Software in 2017) and Regulation Technologies Limited (acquired by mnAi Data Solutions in 2022).Mike's expertise spans data, analytics, and ML product and governance leadership. At TIKOS, Mike leads a VC-backed team developing technology to test and monitor deep-learning models in high-stakes environments, such as defence and financial services, so they comply with the stringent new laws and regulations.// Related LinksWebsite: https://tikos.tech/LLM guardrails: https://medium.com/tikos-tech/your-llm-output-is-confidently-wrong-heres-how-to-fix-it-08194fdf92b9Model Bias: https://medium.com/tikos-tech/from-hints-to-hard-evidence-finally-how-to-find-and-fix-model-bias-in-dnns-2553b072fd83Model Robustness: https://medium.com/tikos-tech/tikos-spots-neural-network-weaknesses-before-they-fail-the-iris-dataset-b079265c04daGPU Optimisation: https://medium.com/tikos-tech/400x-performance-a-lightweight-open-source-python-cuda-utility-to-break-vram-barriers-d545e5b6492fHyperbolic GPU Cloud: app.hyperbolic.ai.Coding Agents Conference: https://luma.com/codingagents~~~~~~~~ ✌️Connect With Us ✌️ ~~~~~~~Catch all episodes, blogs, newsletters, and more: https://go.mlops.community/TYExploreJoin our Slack community [https://go.mlops.community/slack]Follow us on X/Twitter [@mlopscommunity](https://x.com/mlopscommunity) or [LinkedIn](https://go.mlops.community/linkedin)] Sign up for the next meetup: [https://go.mlops.community/register]MLOps Swag/Merch: [https://shop.mlops.community/]Connect with Demetrios on LinkedIn: /dpbrinkmConnect with Mike on LinkedIn: /mike-oaten/Timestamps:[00:00] Regulations as Opportunity[00:25] Regulation Compliance Fun[02:49] AI Act Layers Explained[05:19] Observability in Systems vs ML[09:05] Risk Transfer in AI[11:26] LLMs and Model Approval[14:53] LLMs in Finance[17:17] Hyperbolic GPU Cloud Ad[18:16] Stakeholder Alignment and Tech[22:20] AI in Regulated Environments[28:55] Autonomous Boat Regulations[34:20] Data Compliance Mapping[39:11] Data Capture Strategy[41:13] EU AI Act Insights[44:52] Wrap up[45:45] Join the Coding Agents Conference!

Paulo Vasconcellos is the Principal Data Scientist for Generative AI Products at Hotmart, working on AI-powered creator and learning experiences, including intelligent tutoring, content automation, and multilingual localization at scale.Join us at Coding Agents: The AI Driven Developer Conference - https://luma.com/codingagentsMLOps GPU Guide: ⁠https://go.mlops.community/gpuguideJoin the Community: https://go.mlops.community/YTJoinInGet the newsletter: https://go.mlops.community/YTNewsletter// Abstract“Agent as a product” sounds like hype, until Hotmart turns creators’ content into AI businesses that actually work.// BioPaulo Vasconcellos is the Principal Data Scientist for Generative AI Products at Hotmart, where he leads efforts in applied AI, machine learning, and generative technologies to power intelligent experiences for creators and learners. He holds an MSc in Computer Science with a focus on artificial intelligence and is also a co-founder of Data Hackers, a prominent data science and AI community in Brazil. Paulo regularly speaks and publishes on topics spanning data science, ML infrastructure, and AI innovation.// Related LinksWebsite: paulovasconcellos.com.brCoding Agent - Virtual Conference: https://home.mlops.community/home/events/coding-agents-virtual ~~~~~~~~ ✌️Connect With Us ✌️ ~~~~~~~Catch all episodes, blogs, newsletters, and more: https://go.mlops.community/TYExploreJoin our Slack community [https://go.mlops.community/slack]Follow us on X/Twitter [@mlopscommunity](https://x.com/mlopscommunity) or [LinkedIn](https://go.mlops.community/linkedin)] Sign up for the next meetup: [https://go.mlops.community/register]MLOps Swag/Merch: [https://shop.mlops.community/]MLOps GPU Guide: https://go.mlops.community/gpuguideConnect with Demetrios on LinkedIn: /dpbrinkmConnect with Paulo on LinkedIn: /paulovasconcellos/Timestamps:[00:00] Hotmart Data Science Challenges[02:38] LLMs vs spaCy[11:38] Use Cases in Production[19:04] Coding Agents Virtual Conference Announcement![29:27] ML to AI Product Shift[34:49] Tool-Augmented Agent Approach[38:28] MLOps GPU Guide[41:24] AI Use Cases at Hotmart[49:34] Agent Tool Access Explained[51:04] MLOps Community Gratitude[53:22] Wrap up