What is an agent registry?

By Aston Whiteling, Senior Product Marketing Manager

Autonomous systems are replacing static models and task-specific chatbots.

In their place? A host of autonomous agents.

The proliferation is reaching a critical mass across most enterprises, and the infrastructure assumptions that worked for those earlier deployments no longer rings true, leading to some recurring questions:

“Where did this Agent come from?”

“What can it do?”

“Who owns it?”

An agent registry is the answer to all three.

It’s a centralized system for discovering, managing, and governing AI agents across an enterprise ecosystem, no matter where they’re deployed. They’re the single source of truth for agentic assets, making every autonomous entity searchable, auditable, and compliant.

You can't manage what you can't see, and an agent registry provides much needed visibility into rapidly deploying agentic assets, turning an experimental collection of autonomics into a coordinated, production-grade environment.

They’re a vital component of AI Control Planes, an emerging technology for managing the explosion of agents across almost every business, and play a critical part of governance as we move further into the agentic era.

Governance in the agentic era isn't a brake on velocity, it's actually what makes velocity sustainable. Without a central repository, there's no reliable way to audit an autonomous system's decision trail, trace its data access, or verify who authorized what.

A registry addresses this through four pillars:

• Capability discovery: Every entry defines exactly what an agent can and cannot do, preventing it from being repurposed for tasks it wasn't designed for.
• Identity management: Agents get unique identities, similar to how API management handles service identities. When an agent calls a database, the system knows precisely which one is making the request.
• Auditability: A record of each agent's logic and constraints gives compliance teams a clear trail -- essential in environments where data privacy is non-negotiable.
• Permissioning: Access to specific agents or tools is gated at the registry level, stopping unauthorized users or other agents from triggering sensitive workflows.

A registry is more than a list. It's the infrastructure layer that makes autonomous agent discovery possible at scale, which means it has to handle complex schemas, state management, and evolving agent behavior.

For an agent to be usable by other systems, its intent has to be machine-readable. That means indexing the agent's goal, the tools or functions it can call, and the constraints on its operational logic. Weak metadata produces noisy search results, and noisy results make automated discovery unreliable.

The registry manages the handshake between an agent and an AI gateway, the governance tool that applies security and access management for all AI interactions. Permissions are attached at the entry level, so an agent built for public-facing tasks can't, for example, accidentally reach private CRM data. That boundary is the difference between a controlled deployment and a major data incident.

Agents change. A new model version or prompt update can shift behavior meaningfully, and any dependent agent relying on a specific API version needs to know about it. Lifecycle management within the registry tracks agents from development to retirement, so updates don't silently break downstream workflows.

In a multi-agent system, agents need to find each other. Collaboration across agents is what makes complex task completion possible, and MuleSoft projects that multi-agent adoption will grow by 67% by 2027. The registry is what makes that coordination tractable.

The discovery and handshake process works like this:

1. Request: An orchestrator agent hits a sub-task it can't complete alone.
2. Lookup: It queries the registry for an agent with the required capability.
3. Validation: The registry checks whether the requesting agent has authority to delegate to the sub-agent.
4. Handshake: The registry returns the endpoint and communication protocols needed for the two agents to interact.
5. Execution: The agents complete the task, typically via agent-to-agent (A2A) protocols.

Without this, every interaction would need to be hardcoded, which as we’ve learnt from decades of IT asset management, simply does not scale.

Centralizing agents now is the defining infrastructure investment of the agentic era.

Reuters Institute reports that 75% of technology executives expect agentic tools to have a seismic impact on their industry in the near term. Managing that impact requires the appropriate tools to meet the new challenges of the era.

Solutions like Agent Fabric, MuleSoft’s AI Control Plane, provide the connectivity layer needed to bridge disparate models and enterprise data, turning a collection of AI assets into a coherent agentic workforce. The registry is the foundation that makes that coherence durable as the number of agents, teams, and use cases grows.