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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
By Demetrios4.6
2323 ratings
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

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