Omnigent: Composition, Control, and Collaboration for AI Agents

Omnigent: Composition, Control, and Collaboration for AI Agents

Author: Demetrios July 3, 2026 Duration: 58:16

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 moment


If 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-agents

Omnigent GitHub: https://github.com/databricks/omnigent

Denny Lee on LinkedIn: https://www.linkedin.com/in/dennyglee

Denny's blog: https://dennyglee.com

Tokenomics 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


Hosted by Demetrios, MLOps.community is a space for honest, meandering talks about the real work of making artificial intelligence systems actually work. This isn't about hype or theoretical papers; it's about the messy, practical, and often surprising journey of taking models from a notebook into a live environment. You'll hear from engineers and practitioners who are in the trenches, discussing the tools, the frustrations, and the occasional breakthroughs that define the day-to-day. The conversations are deliberately relaxed, covering everything from traditional machine learning pipelines to the new world of large language models and even the intangible "vibes" of team culture and process. Each episode peels back a layer on what "production" really means, whether that involves deploying a predictive service, managing an agentic system, or maintaining reliability as everything scales. Tuning into this podcast feels like grabbing a coffee with colleagues who aren't afraid to dig into the technical nitty-gritty while keeping the tone conversational and accessible. It's for anyone who builds, manages, or is just curious about the operational backbone that allows AI to deliver value, offering a grounded perspective often missing from the broader conversation.
Author: Language: en-us Episodes: 50

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