Every serious engineering team running AI agents today faces the same quiet chaos: multiple coding agents open in separate tabs, context copy-pasted between Claude Code, Codex, and internal tools, and no clean way to govern what any of them are actually doing. Databricks just open-sourced Omnigent, a new layer they call a meta-harness -- a coordinator that sits above all your existing agent tools and gives them a shared interface for composition, control, and collaboration.

Omnigent targets the problems where a single harness stops: it adds easy ways to compose multiple agents, control them with advanced policies, and collaborate live with teammates. The project is released under Apache 2.0 and is available today in alpha on GitHub.

The problem with the harness-per-agent world

At Databricks, engineers often have 4-5 agents open at once -- coding agents, Gemini search, and others -- spending their time copy-pasting text between them and Docs, Slack, and other collaboration tools. The deeper issue is structural: LLM capabilities are wrapped into an agent harness, and these harnesses have different interfaces that make combining them or swapping them difficult.

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. Databricks' own Genie uses different LLMs for planning, search, and code generation. The pattern is clear: multi-agent, multi-harness pipelines are where the performance gains live. But no single harness can manage that.