Humans in the Loop

Every session, your agent wakes up with amnesia — the same mistakes, the same rediscovery, the same wasted tokens. Memory is how teams solve this, but as the hosts of Agentic AI in DevOps argue, it is both a superpower and a liability. Andrey goes so far as to say that semantic memory makes an already non-deterministic LLM "even less deterministic," while Fernando warns that a poisoned memory file is trusted implicitly — just like a human trusts their own recollections. In this final episode of the foundations series, the hosts unpack the full memory lifecycle — capture, management, retrieval — and share hard-won lessons from building B.O.R.I.S, their agentic DevOps teammate where memory plays a central role. Episode page (show notes and links): https://getboris.ai/insights/007-when-agent-memory-helps-and-when-it-hurts/

LLM subsidies are drying up, subscription limits are tightening, and the astronomical data center CapEx has to be paid by someone — spoiler: it is you. In this episode, Fernando, Andrey, and Vladimir tackle what they call "the big squeeze": the two-sided pressure of rising AI costs and emerging local-inference technologies that could reshape how teams budget for and deploy AI. Along the way, they debate whether buying a Mac Mini is a rational investment or just hype, reveal the mental gymnastics required to get meaningful work out of a twenty-dollar subscription, and argue that if your AI vendor has no pricing page, that tells you everything you need to know about their business model. Episode page (show notes and links): https://getboris.ai/insights/006-the-big-ai-squeeze/

Imagine your agent just deleted a production database — could you have stopped it? The hosts argue that yes, three lines of bash in a single hook could have prevented it, and yet most teams have never configured one. In this episode, Andrey, Vladimir, and Fernando pull apart the agentic loop — the repeating cycle of reason, act, observe that makes coding agents appear to "think" — and show exactly where hooks slot in to give humans deterministic or automated harness-level control over non-deterministic AI behavior, depending on the hook type. Along the way, they unpack Anthropic's rapid-fire release week (Managed Agents, Routines, Opus 4.7, and the advisor tool pattern), debate whether LLM provider commoditization is real or an illusion, and warn that the same hook mechanism that protects your infrastructure can be weaponized through supply chain attacks. Episode page (show notes and links): https://getboris.ai/insights/005-stop-your-agent-before-it-breaks-prod/

A packaging mistake exposed Claude Code's full source tree to the world — and instead of scandal, the community got a masterclass in how agentic coding tools actually work under the hood. In this episode, Andrey, Vladimir, and Fernando unpack what the disclosure revealed about the engineering behind coding agents, introduce the emerging discipline of "harness engineering," and argue that the model is only the horsepower — it is the harness that determines whether the agent gallops toward the right destination or off a cliff. Along the way, they weigh in on NVIDIA's OpenShell sandboxing, Cursor 3.0's agent-first interface, and why Docker containers are not the security blanket many DevOps engineers assume them to be. Episode page (show notes and links): https://getboris.ai/insights/004-harness-engineering-what-claude-code-accidentally-taught-everyone/

What happens when your AI coding tool quietly starts billing like a cloud service — and your team burns through a thousand dollars in a week? Vladimir returns as the hosts share their sticker-shock moment with Cursor's new pricing before diving into agent skills. From Claude Code skills to Kiro Powers to AWS Strands SOPs, the naming varies but the idea is the same — plugging structured knowledge into an agent's brain on demand. Episode page (show notes and links): https://getboris.ai/insights/003-skills-powers-sops/

What happens when your AI coding assistant forgets what it was just working on? Andrey and Fernando break down context windows, how MCP servers can consume a large share of the session before the first message, and why over-specifying agent behavior in project rules often hurts output. Episode page (show notes and links): https://getboris.ai/insights/002-the-tool-layer-what-makes-agentic-ai-possible/

What if most AI tools sold into DevOps fail because they only see part of the stack? In this first episode of Agentic AI in DevOps, Andrey and Fernando trace AI tooling from ChatGPT's launch in late 2022 through 2025: they treat context—not model hype—as the constraint, and explain why assistants cut off from source, logs, metrics, and docs tend to guess wrong under real operational load. Episode page (show notes and links): https://getboris.ai/insights/001-ai-in-devops-2022-to-2026-from-autocomplete-to-action/