Introducing Omnigent: A Meta-Harness to Combine, Control and Share Your Agents

2026-06-13 · Source: Databricks · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Software Development & Engineering, Robotics & Autonomous Systems · Depth: Intermediate, short

Summary

Databricks has open-sourced Omnigent, a new meta-harness designed to unify and enhance the functionality of diverse AI agents. Addressing the current fragmentation where users juggle multiple agents and builders struggle with incompatible harnesses, Omnigent provides a layer above existing tools like Claude Code, Codex, and custom agents. It wraps any agent in a sandboxed session with a uniform API, enabling seamless composition of multiple models and harnesses without code rewrites. The system also offers robust control through stateful, contextual policies for cost budgets and dynamic security, such as requiring human approval after package downloads or restricting write access. Furthermore, Omnigent facilitates real-time collaboration, allowing teammates to share live agent sessions, review files, and steer agents together. It supports cloud execution on platforms like Modal and Daytona and offers multiple interfaces (web, mobile, desktop apps, APIs). Omnigent is released under Apache 2.0, aiming to establish a new abstraction layer for agent interaction.

Key takeaway

For AI Engineers building multi-agent systems or struggling with harness incompatibility, Omnigent offers a critical abstraction layer. You can now compose diverse agents, enforce granular security and cost policies, and enable real-time team collaboration without rewriting code for each harness. Consider integrating this Apache 2.0 open-source meta-harness to streamline your agent workflows and enhance operational control, especially when deploying agents in sandboxed cloud environments.

Key insights

A meta-harness unifies disparate AI agents, enabling composition, advanced control, and collaboration across different models and harnesses.

Principles

• Agent engineering benefits from higher-level abstraction.
• Meta-harnesses enable cross-harness agent composition.
• Dynamic policies enhance agent control and security.

Method

Omnigent wraps command-line agents and SDKs with a common API, providing sandboxed sessions. A server then applies policies for control, collaboration, and cloud execution.

In practice

• Combine diverse agents like Claude Code or custom agents.
• Enforce dynamic cost and security policies on agent actions.
• Share live agent sessions for real-time team collaboration.

Topics

• AI Agents
• Multi-agent Orchestration
• Omnigent Meta-harness
• Open-Source Software
• Agent Security
• Cloud Sandboxes

Code references

• omnigent-ai/omnigent

Best for: AI Architect, CTO, VP of Engineering/Data, AI Engineer, Machine Learning Engineer, MLOps Engineer

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Editorial summary, takeaway, and curation by AIssential. Original article published by Databricks.