A Common Layer Above Fragmented AI Agents
Databricks has released Omnigent, an open-source "meta-harness" engineered to bring disparate AI coding agents together under a single interoperable layer. The project responds to a problem that has grown sharper as engineering teams adopt several agent tools at once: fragmentation. When developers run multiple agents side by side, coordinating them becomes difficult, and Omnigent is positioned to solve exactly that challenge.
Rather than replacing the tools developers already use, Omnigent sits above them. It operates as a layer over established AI agents — including Claude Code, Codex, and Pi — and offers a shared interface for composing, controlling, and collaborating across all of them. Released under the Apache 2.0 license, the tool lets developers swap or combine harnesses without rewriting code, run agents either locally or inside sandboxed environments, and enforce cost budgets and data-centric policies at the meta-harness layer instead of relying on prompts to do that work.
The motivation came directly from observed behavior. Databricks pointed to a trend in how AI was being used internally and at Neon: engineers were stitching multiple agents into loops and workflows, and managing that orchestration above the harness layer was proving difficult. Omnigent is the response to that friction.
Inside the Three Pillars: Composition, Control, and Collaboration
Omnigent is organized around three core pillars, each addressing a distinct part of the multi-agent workflow.
Composition
Composition makes it possible to assemble multi-agent teams that draw on different harnesses or models. Switching between them takes a single one-line change, so developers can reconfigure which agents work together without reworking their entire setup.
Control
Control brings governance into the workflow through stateful policies and budget constraints. These are enforced at the meta-harness level rather than buried inside prompts, giving teams a more reliable way to cap spending and apply consistent rules across the agents they run.
Collaboration
Collaboration turns an individual agent session into a shared one. Teammates can open a live session through a URL that carries the full history, which makes real-time review and steering possible. That access isn't tied to a single environment either — participants can join from a terminal, the web, a desktop app, or a phone.
Alpha Availability and Installation
Omnigent is currently available in alpha and can be installed with a single command, lowering the barrier for developers who want to test it quickly. The project is fully public: a GitHub repository lives at omnigent-ai/omnigent, and a documentation site is online at omnigent.ai.
Timing, Context, and Databricks' Agent Infrastructure Strategy
The release lands just before the Databricks Data + AI Summit 2026, scheduled for June 15–18 in San Francisco — placing Omnigent in front of a large, relevant audience at an opportune moment.
It also fits into Databricks' wider expansion into enterprise agent infrastructure. That effort includes the general availability of Agent Bricks, the company's governed enterprise agent platform announced in April. The two products serve different needs, however. Agent Bricks concentrates on enterprise governance, data context, and model routing within the Databricks platform. Omnigent stakes out a separate niche: an open-source tool aimed at individual developers and teams who work across multiple external agent harnesses.