APAI 2502 - AI Agent Registry
What is an AI Agent Registry
AI systems are evolving to be more modular and collaborative, with specialized functions aimed to handle specialized tasks - data I/O, workflows, or monitoring other systems. These specialized functions, as used inside AI enabled systems, are called agents; at a base level these are simple functions such as “printf(‘Hello World’)” - people that know of the original C programs, will recognize this. Deploying, operating, managing, discovering these agents is a difficult and complex task.
This is where an AI Agent Registry comes in.
What is AI Agent Registry
An app registry is a centralized catalog of apps - such as the Apple App iOS Catalog, where people can use to discover, upload, deploy apps.
AI agents are programs that can execute specific tasks. AI agents are specialized, they perform focused tasks and they need a method to be discoverable. This is a significant problem at scale, where organizations deploy more and more agents; teams need a way for their apps and other agents to discover each other, share capabilities, and integrate into workflows.
An AI Agent Registry serves as a centralized (or federated) catalog of running agents and their metadata, similar to a model registry This registry enables capability discovery and orchestration: agents (or humans) query the registry to find the right agent for a task, inspect its abilities, and obtain connection details.
There is no standardization for core features of a registry; however, there are some foundational functionality which is basic: that is catalog capabilities - registration of agents, updating, versioning, deletion, discovery.
Deployment is NOT basic/core functionality of a registry; all proper registry systems are careful to separate routing functionality. Many articles tend to conflate registry capabilities with routing or deployment. That is not a core functionality of a registry, but an add on and often the selling proposition of SaaS systems.
Agent registries have become a critical enterprise AI system architecture component for enabling agent interoperability and agent governance across departments.
AI Agent Registry Core Functionality
Implementations of AI Agent Registries varies, but they should all offer following:
1. Agent Registration: Before we do anything with an agent, we must register the agent. This is a simple database record that registers a unique id/name and metadata about the agent. This can be done via UIs, REST API, bulk uploading, etc. This agent metadata - which can be called an agent card - will include attributes such as name, description, endpoint URL, version, keywords about capabilities, input/output parameters.
This absolute core functionality is what is often conflated in articles online: a registry is nothing but a set of records in a data store; these are NOT deployed agents and there is a huge difference between deployed agents and entries in a system.
For example, a simple agent can be just a prompt that can be used with an AI model. Here is an example of parameters for a simple agent that says “Hello AI Agentic World”:
Name: hello_world_agent
Prompt: “You are an agent that always answers with ‘Hello AI Agentic World’.”
This agent does not need deployment, because it can be injected at runtime as part of the AI prompt; it can also be listed, prompt can be displayed to the end user who will copy/paste it for their own use. A registry is a listing of agents, but does NOT imply deployed, running agents.
We could add to above agent an endpoint entry where it is deployed, however, routing to that agent is an add on functionality beyond core functionality. (And yes, many SaaS systems offer routing capabilities).
2. Discoverability: Search is the next capability that an AI Registry will implement. Users, systems, other agents need a method to discover AI agents in the registry. Users can search the registry to find agents by capability, tag, or keyword. While this can get complex, at its core this is a simple data search capability.
3 Access Control and Governance: Keeping in line with core functionality of registries (again, not discussing live, deployed systems), access control is fairly standard user access control, typically done via role based authentication (username+password) or secure token access for API system.
4. Metadata Management: The registry will need to maintain metadata for each agent. In addition to base parameters such as name and description, metadata can include endpoint URL, security tokens, OPENAI or other provider API keys - this is especially prevalent for any agents that need access to externally hosted models that charge per token.
Routing Functionality
Registry providers often provide routing capabilities to deployed endpoints. This is most often models that an agent can be injected at runtime and executed (as a prompt) or it can also be deployed agents as stand alone applications.
5. Routing: Agents can contain endpoints where the agent is deployed or where the AI model capable of running the agent (prompt) is deployed. Registries can provide add-on routing capabilities which can help organizations with access standardization.
It is important to understand that there are many routing (gateway) systems that offer routing as their main capability. They can offer registry features as an add-on to their users, but they will make it as their bread-n-butter to provide routing capabilities.
This means, a registry and a gateway are two different things and are not to be confused. They often work hand in hand to provide a vertically integrated system.
5. Health Monitoring & Heartbeats: For deployed agents, a registry can maintain a hearbeat which verifies that an agent is available for requests. This is useful for apps accessing agents that are supposed to be online, it keeps the listing of live agents up to date and ensures endpoint provider maintains a healthy set of agents if they want to continue to be listed in the registry.
6. Audit Logging and Observability: Gateway systems tend to implement observability and logging as second core functionality (to the routing function). This goes beyond core metrics as the system will keep track of usage. Agent authors can get stats about when an agent is used, how often, and other useful anonymized stats. This is also helpful for teams to understand usage patterns and debug integration issues. This data can be aggregated at platform level, especially for enterprise systems such higher level usage metrics are mission critical to have.
What have we learned?
An AI registry is a specialized catalog for AI agents.
An AI registry is NOT a routing system.
How a registry is implemented, and the extra functionality such as advanced discoverability, routing, heartbeat monitoring, etc are choices a provider makes and I will cover that in other articles.