How do agents discover other agents in a multi-agent system?

Agent discovery is the process of finding which agents exist in a multi-agent system, what they can do, and how to contact them. Instead of hard-coding every possible collaborator, a requesting agent or orchestrator can consult a trusted directory and select an appropriate available specialist.

Beginner-friendly example: API test generation

Suppose a user asks an AI testing platform to generate security tests for a payment API. The platform has several specialists: a requirements extractor, a functional test generator, a security test generator, and a performance test runner.

The coordinating agent first looks for agents with the capability security_test_generation. A registry returns two eligible candidates:

• Agent A: Supports authentication and authorization tests; currently available.
• Agent B: Supports security tests but is busy.

After verifying permissions and availability, the coordinator sends the scoped task to Agent A.

Request → Search agent registry → Filter capabilities → Check permissions/health → Select agent → Send task

Discovery finds a suitable collaborator; it does not itself guarantee successful execution.

How can agents discover each other?

• Static configuration: A small system uses a predefined list of agent names and endpoints. Simple and predictable, but manual updates are needed.
• Central agent registry or directory: Agents publish metadata such as identifiers, capabilities, supported interfaces, and endpoints. Requesters search the directory.
• Service discovery: Infrastructure mechanisms locate running agent services and monitor their current endpoints or health.
• Capability-based lookup: The requester searches for required skills or task types rather than knowing a specific agent name in advance.
• Federated or decentralized discovery: Multiple directories or peers share discovery information under defined trust rules. This is more flexible but harder to secure and keep consistent.

What should an agent record contain?

An agent directory entry might include:

• Agent ID and version: A stable identity and implementation version.
• Capabilities: Tasks supported, such as generating boundary-value API tests.
• Input/output contract: Accepted schemas, supported protocols, and expected outputs.
• Endpoint or routing key: Where permitted tasks can be sent.
• Availability: Health, current capacity, or queue status, when maintained.
• Trust information: Owner, allowed tenants, required authentication, and authorization policy.

Treat capability advertisements as claims that require validation, not proof of competence. Availability information can become stale between discovery and dispatch.

Discovery vs. task allocation

Discovery answers “Which agents could do this job?” Allocation answers “Which candidate should receive it now?”

For example, discovery may return three security agents. A subsequent scheduler or Contract Net Protocol negotiation can choose one based on capacity, latency, deadline, or bid. Keeping these stages distinct makes the architecture easier to reason about.

LangGraph and Celery implementation idea

In a LangGraph-based system, a routing node can query an approved agent registry and select from an allowlist of graph nodes, subgraphs, or externally callable services. If specialists execute as Celery tasks, the registry can map capabilities to approved task types or queues.

Do not let an LLM invent arbitrary task names, URLs, or executable endpoints. Validate routing decisions against trusted configuration, apply tenant-specific permissions, and use timeouts and fallback paths when an agent is unavailable.

In short

Agent discovery helps multi-agent systems locate suitable collaborators dynamically using identities, capability descriptions, and trusted routing information. Effective discovery separates finding an agent from authorizing, assigning, and validating its work.