AI Agent Registry: How Enterprises Discover, Register, and Govern Autonomous Agents

Enterprises cannot govern AI agents they do not know exist. But discovery alone is not governance.

An AI agent registry is an enterprise system of record that assigns each autonomous agent a persistent identity and connects it to its owner, purpose, deployment environment, version, model, provider, credentials, tools, data access, permitted actions, autonomy level, risk classification, policy bindings, lifecycle status, runtime activity, incidents, audit evidence, and revoke path.

This is more operational than a conventional AI inventory. An inventory helps an organization understand which AI systems and use cases it has. A registry goes deeper by maintaining an identity-level record for each autonomous actor and connecting that record to live controls.

The central principle is simple: the AI agent registry is the system of record for Agentic Identity, and AIP makes that record operational.

An AI agent registry is an enterprise system of record that maintains a persistent identity and governance record for every autonomous AI agent.

A mature registry records which agents exist, where they run, who is accountable for them, what they are intended to do, which systems and tools they can access, what actions they are permitted to perform, what credentials they use, which policies apply, what risk they present, what they have done, and how they can be suspended or revoked.

It should be continuously updated from both administrative and runtime sources. A registry that depends only on developers manually updating a spreadsheet will quickly become stale as models, tools, permissions, owners, and deployment paths change.

The registry becomes especially valuable when multiple teams deploy agents through different frameworks, cloud environments, SaaS applications, orchestration platforms, MCP servers, A2A interfaces, scheduled workflows, and shared service accounts.

AI inventory means the broader list of AI systems, models, applications, or use cases used by the organization. It is essential, but it usually operates at the system level rather than the autonomous actor level.

Agent discovery is the process of detecting that an autonomous or semi-autonomous agent exists by observing signals such as endpoints, frameworks, service accounts, MCP activity, model-provider traffic, or workflow behavior. Discovery produces a candidate record, not an approved identity.

An A2A Agent Card is a self-describing manifest used for discovery and interoperability. It can describe capabilities, interfaces, security schemes, supported modes, and skills. It is useful, but it is not the same as an internal enterprise governance record.

An agent registry is the enterprise system of record containing persistent identity, ownership, purpose, deployment context, lifecycle status, risk, permissions, credentials, controls, operational relationships, and audit evidence.

An AIP identity record is the governable record that connects the agent's persistent identity to accountable owners, runtime policy, credentials or keys, evidence, rotation history, suspension, and revocation.

Enterprise AI estates are moving beyond a small number of centrally approved chatbots. Agents now emerge through several channels simultaneously: internally developed agents, SaaS-embedded assistants, developer-created workflows, vendor-operated agents, multi-agent systems, and agents hidden behind shared service accounts or workload identities.

MCP and A2A improve connectivity and interoperability, which is useful. They also make it easier for agents to interact with more tools, resources, and other agents. That increases the importance of knowing exactly which governed agent is acting and under which enterprise boundaries.

Workload identity remains essential, but it does not automatically provide an agent-specific record of purpose, autonomy, ownership, or action context. A registry closes that gap by connecting the technical subject to the governed autonomous actor.

Without a common registry, the same organization may end up with disconnected fragments: an IAM record for a service principal, a source repository for agent code, an AI inventory entry for the larger system, an A2A Agent Card for interoperability, cloud logs for the runtime, and a risk assessment somewhere else. The registry connects those fragments into one governable record.

An AI inventory and an AI agent registry should complement each other. The inventory answers questions at the system or use-case level. The registry answers questions at the autonomous actor level.

One AI system may contain several agents, and one agent may participate in several workflows. That is why the portfolio record alone is not enough once agents start acting independently across tools and systems.

A spreadsheet can be a reasonable starting point because it is accessible and easy to customize. The problem begins when it is treated as the final control plane.

Static inventories become incomplete because agents change faster than manual governance records. An agent can be redeployed into another environment, receive a new model, gain another MCP server, inherit a broader service account, add a write-capable tool, change ownership, stop producing telemetry, or remain active after the project is closed.

A useful registry therefore needs declared information, imported information, and observed information. Declared information comes from developers, owners, vendors, and governance teams. Imported information comes from IAM, cloud, code, CMDB, procurement, and model-catalog systems. Observed information comes from runtime activity such as tool calls, endpoints reached, policy decisions, credential usage, incidents, and last activity.

A registry that combines declared, imported, and observed data is much harder to ignore and much less likely to become stale.

Discovery and registration are separate stages. Discovery identifies a signal suggesting that an agent exists. Registration validates that candidate and creates a trusted enterprise identity record.

Discovery signals may come from model-provider API calls, A2A Agent Cards, MCP client activity, service accounts, OAuth clients, source repositories, orchestration frameworks, scheduled workflows, network telemetry, cloud deployments, browser controls, procurement records, or employee declarations.

A discovery signal can be high confidence and still not answer the governance questions that matter: what the workload is, who owns it, whether it is approved, whether the credential is shared, what data it processes, and what controls should apply.

Registration is therefore the moment when the enterprise moves from candidate detection to accountable identity. Governance then decides whether the agent may operate, under what policies, and with which reviews. Runtime enforcement applies those policies when the agent actually acts.

A useful registry needs enough structure to support ownership, review, runtime control, investigation, and revocation. It should connect administrative identity data to actual observed operation.

The exact schema varies by organization, but the record should cover identity, ownership, purpose, deployment context, technical bindings, permissions, risk, lifecycle, runtime state, incidents, evidence, and revocation.

Organizations starting with a smaller registry should still capture a minimum useful record. That baseline is enough to avoid turning the registry into a name-only list.

Persistent Agent ID

Agent name and version

Parent AI system

Environment and hosting location

Lifecycle status

Business owner, technical owner, and security owner

Intended purpose and prohibited uses

Autonomy level and risk tier

Model and provider

Service account or workload identity

Connected tools and systems

Permitted data classes and actions

Policy bindings

Last activity and next review date

Revocation method

The registry becomes most valuable when connected to AIP. The registry stores the persistent governable identity record. AIP uses that record to resolve the agent's owner, purpose, status, scope, risk, policy bindings, credentials, and audit context at runtime.

That means the registry is not just an inventory table. It becomes the source of truth a control layer can reference when deciding whether to allow, deny, warn, mask, hold, approve, or escalate a proposed action.

This is where the registry connects to the broader architecture described in What Is Agentic Identity Protocol?, The AI Agent Identity Lifecycle, AI Agent Permissions, and AI Agent Observability.

An AI agent registry is therefore not the end of governance. It is the system of record that makes governance actionable.

Define what qualifies as an AI agent in the organization.

Define the relationship between AI systems, agents, versions, credentials, and runtime events.

Assign a registry owner and mandatory ownership roles.

Create a standard lifecycle state model and review cadence.

Identify available discovery signals across logs, IAM, code, cloud, MCP, A2A, procurement, and declarations.

Require a persistent Agent ID for every production agent.

Map service accounts, workload identities, OAuth clients, keys, and certificates back to the registry record.

Define permitted systems, tools, data classes, and actions.

Bind runtime policies and approval thresholds to the identity record.

Detect activity from suspended or revoked agents.

Preserve policy decisions, approvals, overrides, and incidents as evidence.

Test end-to-end suspension and revocation instead of assuming they work.

What is an AI agent registry? An AI agent registry is an enterprise system of record that assigns each autonomous agent a persistent identity and connects it to ownership, purpose, deployment context, permissions, policies, credentials, risk, lifecycle status, runtime activity, evidence, and revocation.

What is the difference between an AI inventory and an AI agent registry? An AI inventory usually records AI systems, models, applications, and use cases. An AI agent registry records the identity and operational governance context of individual autonomous agents within or across those systems.

How do companies discover AI agents? Companies can discover agents through model-provider logs, API gateways, SDK instrumentation, IAM identities, OAuth clients, MCP activity, A2A Agent Cards, source repositories, cloud resources, workflow platforms, browser controls, network telemetry, procurement records, and employee declarations.

Is an Agent Card the same as an agent identity? No. An A2A Agent Card contains identity-related metadata and describes how an A2A agent can be discovered and contacted, but it is not the same as a complete internal enterprise governance record.

Does AIP replace A2A? No. A2A supports interoperability and communication between agents. AIP adds an enterprise identity and governance layer that connects agents to ownership, scope, runtime policy, evidence, and revocation.

Does AIP replace IAM or workload identity? No. IAM and workload identity authenticate applications, services, and software workloads. AIP connects the autonomous agent to those technical identities while adding agent-specific purpose, ownership, scope, policy, and action-level evidence.

How do you assign an owner to an AI agent? At minimum, assign a business owner responsible for the use case, a technical owner responsible for operation and deployment, and a security or governance owner responsible for controls and review.

How do you find unmanaged AI agents? Start by correlating model-provider traffic, service accounts, OAuth clients, MCP activity, A2A Agent Cards, AI-related source code, cloud workloads, workflow platforms, procurement records, browser activity, and employee declarations. Candidate agents should then be validated and either registered, restricted, remediated, or revoked.

What should an AI agent registry contain? It should contain identity, ownership, purpose, technical configuration, workload identities, credentials, systems, tools, data access, permitted actions, autonomy level, risk, policy bindings, lifecycle state, review status, activity, incidents, evidence, and revocation information.

Why does an agent need a persistent Agent ID? A persistent Agent ID lets an enterprise connect the same agent to policies, credentials, versions, runtime events, incidents, reviews, and historical evidence without relying only on a mutable name, IP address, repository, or service account.

What is a Shadow Agent? A Shadow Agent is an autonomous or semi-autonomous AI agent developed, deployed, connected, or used without sufficient central visibility, ownership, approval, or governance.

How does AIP use an AI agent registry? AIP uses registry data to resolve the agent's identity, owner, status, scope, risk, policy bindings, credentials, and audit context so a runtime control layer can decide whether to allow, deny, mask, warn, hold, or escalate an action.

Why should revoked agent records be retained? Historical records may be needed to verify older logs, investigate incidents, understand which credentials were active, and prove when and why an agent was revoked.

Can a registry automatically discover every agent? No single discovery mechanism can reliably identify every agent. The strongest approach combines automated telemetry, IAM and infrastructure data, protocol metadata, source analysis, procurement records, and human declarations.

Agentic Identity Protocol page

AgentID Platform

AgentID Security

What Is Agentic Identity Protocol?

The AI Agent Identity Lifecycle

AI Agent Permissions

AI Agent Identity vs Machine Identity

The Agentic Protocol Stack

AI Agent Observability

A2A protocol documentation

Model Context Protocol intro

SPIFFE overview

NIST AI Risk Management Framework 1.0