How can I apply the same policies across multiple coding agents?

Summary

• Route every coding agent through one control plane. When each agent's model and tool calls pass through the same gateway, one set of policies covers all of them, whatever the vendor or harness.
• Govern the assets, not the app. Register the models, tools, and MCP services agents use as catalog objects, then grant and revoke access with the same privileges you use for data, so a new agent inherits the existing rules.
• Enforce identity and limits at the gateway. Each request resolves the caller, checks authorization, applies rate limits, and runs safety guardrails before it reaches a model, so one policy set holds everywhere.
• Get one audit trail and one cost record. Usage tracking logs every request across every agent in system tables, so you attribute usage and spend by user, team, or project from one place.
• On Databricks, Unity Gateway is that control plane (generally available), built on Unity Catalog. It governs models, tools, and agents as securables, so the same permissions, guardrails, rate limits, and logging reach every coding agent that calls through it.

How can I apply the same policies across multiple coding agents?

Put a single control plane in front of every coding agent and route each agent's model and tool calls through it. Once traffic funnels through one point, you write access rules, rate limits, and safety guardrails once and they apply the same way to every agent, whether it's a command-line tool, an IDE assistant, or an in-house script. Wiring up each agent and each provider key on its own guarantees drift: policies diverge tool by tool until no one can say what is allowed where. Governing at the gateway keeps one answer to "what is allowed" however many agents you add.

What does governing across coding agents require?

Coding agents differ from chatbots because they act: they call models, invoke tools, run code, and read data on a developer's behalf, so a loose one can leak secrets or reach systems it should not. Holding many agents to one policy set takes three things in one place: a single identity and permission model, so each agent runs with its user's access; runtime controls such as rate limits, guardrails, and approval gates applied on every request; and a shared record for cost and audit. The clean way to get all three is to govern the assets agents use, the models, tools, and MCP services, rather than policing each agent one by one.

Key capabilities to evaluate

Capability	Why it matters
One control plane for all agents	A single path in front of every agent means policy is enforced once, not re-implemented per tool or per provider.
Assets as governed objects	Registering models, tools, and MCP services as catalog securables lets you grant and revoke access uniformly, and a new agent inherits existing rules.
Caller identity on every request	Agents run with the invoking user's permissions, so access matches the person, not the tool.
Rate limits and quotas	Token and request rate caps per user, endpoint, or key stop any one agent from running away.
Safety guardrails	Filters for sensitive data, unsafe content, and prompt injection apply to every request, not only the agents someone configured.
Provider-agnostic routing	Point agents at any backend model and switch providers without changing the policies that govern them.
Unified usage and audit	One log of every request across every agent for cost attribution and compliance.

How Databricks approaches this with Unity Gateway

Unity Gateway is that control plane, generally available and built on Unity Catalog. It puts one governed path in front of every coding agent: a request resolves the caller through unified authentication, checks Unity Catalog authorization, applies policy and guardrails, routes to a backend model, and logs the result. Because that path is identical for every agent, the controls below apply no matter which coding tool made the call.

• Asset governance. Models, tools, MCP services, and agents are registered as Unity Catalog securables and controlled with grants such as EXECUTE to use one and MANAGE to administer it, the same privileges you already apply to tables and volumes. Grant access once and it holds across every vendor and harness. See how AI governance works on Databricks.
• Traffic governance. Rate limits cap token and request rates per user, endpoint, or key, and the gateway routes across Databricks-hosted and external model providers with fallback. External credentials are held as model provider services, so the gateway supplies them at request time and agents never handle raw keys.
• Behavior governance. Built-in guardrails screen for sensitive data, unsafe content, and jailbreak attempts on every request, and service policies evaluate each call and response to allow, deny, or require approval based on the user, agent, model, or content. These policy features are evolving, so confirm the current availability of a specific control in the docs.

Every request is recorded in usage tracking system tables, so you can attribute usage and cost by user, team, or project across all agents. For the wider design, see Unity Gateway and governing AI agents at scale.

Use cases

• A mixed toolchain under one policy. Teams choose different coding assistants; routing all of them through the gateway keeps one permission model and one audit trail.
• Safe access to internal tools. Agents reach databases and APIs only through governed tools and MCP services, so an assistant can call only what its user is cleared to call.
• Cost control across agents. Rate limits and usage tracking keep any single agent or team from running up model spend unnoticed.

FAQs

Do I have to configure each coding agent separately?

No. Point each agent at the gateway and the policies live there. Access rules, rate limits, and guardrails apply to every agent that routes through it, so onboarding a new one does not mean rebuilding governance.

Does this work with agents that use non-Databricks models?

Yes. The gateway routes to Databricks-hosted and external providers alike, and the same permissions, limits, guardrails, and logging apply regardless of which model a coding agent targets.

How do I keep one agent from exceeding its limits?

Set rate limits and quotas per user, endpoint, or key at the gateway. Usage tracking then shows consumption across agents so you can tighten limits where they are needed.