Introducing Omnigent: A Meta-Harness to Combine, Control and Share Your Agents | Databricks Blog
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- agent harness — The scaffolding around a model that turns it into a working agent — the loop, the tools it can call, and the rules for when to stop.
- AI agent — An AI system that doesn't just answer once but works toward a goal in a loop — taking actions, reading the results, and deciding what to do next.
An Apache-2.0 meta- means you can compose and swap harnesses behind one API instead of rewriting against each one's interface.
“The problem is that LLM capabilities are wrapped into an agent harness, and these harnesses have different interfaces that make combining them or swapping them difficult.”
Databricks
“We adopted coding agents early across our 5,000+ member engineering team and built thousands of agents for customers. That experience convinced us that the frontier of agent engineering is moving up a level.”
Databricks
“The best results no longer come from a single model in a single harness: Harvey beat a frontier model on quality and cost by giving an open-source worker model a frontier advisor it can call, Anthropic built its research product as a lead agent orchestrating parallel subagents, and our own Genie uses different LLMs for planning, search, and code generation.”
Databricks
“The models and harnesses will keep changing as the field evolves; the layer you work at shouldn t have to.”
Databricks
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