Omnigent: Composition, Control, and Collaboration for AI Agents episode artwork

EPISODE · Jul 3, 2026 · 58 MIN

Omnigent: Composition, Control, and Collaboration for AI Agents

from MLOps.community · host Demetrios

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

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Omnigent: Composition, Control, and Collaboration for AI Agents

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