If you have spent any time working with AI agents lately, you already know the problem. One team is using Claude Code. Another is on Codex. Someone else built a custom agent for internal reporting. Each one works well on its own, but none of them talk to each other, none of them share the same rules, and nobody has a single place to see what all of them are doing.
Databricks just released something aimed squarely at that problem. It is called Omnigent, and it is worth understanding, especially if your organization is starting to run more than one AI agent at a time.
Think of Omnigent as a control tower for AI agents. It sits above the tools you already use, things like Claude Code, Codex, Cursor, or agents your own team built, and gives them a common language.
Instead of each agent living in its own silo with its own login, its own rules, and its own interface, Omnigent lets you:
Swap or combine different agents without rewriting your setup
Apply consistent rules and guardrails across every agent, no matter which tool built it
Watch and collaborate on a live agent session from your laptop, phone, or browser, together with your team
Databricks describes it as a meta harness, a layer that sits above individual agent tools the way an operating system sits above individual apps. You define an agent in a short configuration file, and if you want to change which model or tool powers it, you change one line, not your whole workflow.
It is open source and free to use on your own infrastructure. Databricks also offers a managed version that plugs directly into their platform, so companies already using Databricks for their data can extend the same governance and access controls they trust for data straight into their AI agents.
Select an agent to see how a single configuration file establishes governance:
apiVersion: omnigent.databricks.com/v1alpha1
kind: AgentHarness
metadata:
name: claude-code-agent
spec:
runtime:
provider: anthropic
model: claude-3-7-sonnet
governance:
unityCatalogAccess: restricted_schemas
spendLimitMonthlyUSD: 1500
requireApprovalFor: [prod_db_write, git_main_push]Strip away the technical language and the value comes down to three things every enterprise leader cares about.
Right now, most companies running multiple AI agents have no single view of what those agents are doing, what data they can touch, or how much they cost. Omnigent gives you one place to set those rules and watch them get enforced, instead of hoping each tool behaves.
Agents that run wild can run up surprising bills. With Omnigent, you can set spending limits per agent, per team, or per project, and even have it automatically pause or switch to a cheaper option when a threshold is hit.
New agent tools show up every few months. Omnigent is built so you are not stuck with whatever tool you picked eighteen months ago. You can bring in a better one later without rebuilding everything around it.
A few examples of how this kind of governed, multi-agent setup tends to get used in practice:
Financial services, where every action an agent takes needs to be auditable, and access to sensitive data has to follow strict, provable rules.
Healthcare and insurance, where multiple agents might touch patient or claims data, and governance is not optional, it is a compliance requirement.
Software engineering teams, where developers are already juggling several coding agents at once and need one place to coordinate them, review their work, and keep them within safe boundaries.
Customer operations, where support and back office agents from different vendors need to work together without exposing more data than each one needs.
The common thread is simple. As soon as a company moves from one AI agent to several, someone has to own governance, cost, and coordination. That is exactly the gap Omnigent is built to close.
Here is the honest part. Omnigent gives you the primitive, the raw capability. It does not show up out of the box already configured for your compliance requirements, your data access rules, or your business processes. That setup work is where most companies get stuck, and where getting it wrong early costs far more to fix later than to build correctly from day one.
This is exactly the kind of work Abilytics does. As a Databricks Bronze Partner and an AI-first engineering firm, we help enterprises:
Design the identity, access, and governance foundation before the first agent ever runs, not after
Decide when to start with the free, self-managed version of Omnigent versus moving to the managed Databricks deployment for production
Map agent policies to real business processes and compliance requirements, not just technical defaults
Build the custom integrations and workflows that connect Omnigent to your existing systems
If your team is already running more than one AI agent, or planning to, this is the moment to get the governance layer right, before it becomes harder to retrofit.
Let's talk about what a governed, multi-agent setup could look like for your business. Reach out to Abilytics to start the conversation.