What Is an AI Agent Registry?

An AI agent registry is a live, authoritative inventory of every agent operating in the enterprise: what each one is, what it may decide, what it connects to, who owns it, and how it is behaving.

You need one for a reason that sounds too simple to be the foundation of a governance program, and is anyway: you cannot govern a population you cannot enumerate.

Every other control an enterprise might want — policy enforcement, coordination, risk review, incident response — presumes a known population, and in most organizations today the population is not known. The registry is therefore not one control among many. It is the precondition for all of them, and it is the cheapest item on the entire orchestration agenda.

What Should the Registry Contain?

Seven fields cover the essential record for each agent:

• Identity: what the agent is and what business purpose it serves.
• Ownership: the named person accountable for its outcomes.
• Mandate: what the agent is permitted to decide, and at what latitude.
• Connections: the systems it reads and writes, and the other agents it triggers or is triggered by.
• Provenance of build: who created it, on what platform, and sanctioned through what path.
• Status: whether the agent is live, paused, or retired.
• Vital signs: enough behavioral telemetry to know whether it is active and within expected patterns.

The connections field deserves special emphasis, because it converts the registry from a list into a map. The map is what incident response, coordination design, and cascade analysis all consume.

If the registry does not embarrass you, it is not finished.

How Do You Find the Agents You Do Not Know About?

Discovery is the hard half, and it must combine top-down declaration with bottom-up detection.

Declaration alone fails, because the agents most worth finding are the ones nobody thinks to declare: automations embedded in licensed platforms, assistants configured by business users who would never describe what they built as deploying an agent.

Detection means looking at the environment itself: platform admin consoles, integration and API activity, and service accounts with unexplained traffic patterns.

The exercise should be run with a specific expectation: build the registry expecting to be embarrassed. The gap between the believed population and the actual one is not a defect in the exercise. It is the deliverable, and its size is the single most honest measure of how much coordination work lies ahead.

What Does a Registry Exercise Look Like in Practice?

Consider a global tier-one automotive supplier that began a registry effort confident it was running roughly twenty-five agents across quality, procurement, and plant scheduling.

Declaration surfaced thirty-one. Detection surfaced sixty-three, including supplier onboarding automations configured inside a procurement platform three years earlier and still exercising real judgment, with an owner who had left the company.

The supplier’s leadership treated the number exactly right: not as an indictment, but as the first accurate picture the enterprise had ever had of itself.

The registry became the working document for everything that followed: ownership assignments, mandate reviews, and coordination design. The standing rule that made it durable was simple: no agent goes live, on any platform, without a registry entry, and the registry is reviewed as a living document, not archived as a snapshot.

Registry design and agent discovery are early workstreams in Inteq’s Agentic AI consulting engagements, and the analysis disciplines behind them are taught in Inteq’s Agentic AI course catalog. This post accompanies the full white paper on AI agent orchestration as enterprise infrastructure.