The A2A protocol addresses a growing problem in agentic AI: how one AI agent can discover and collaborate with another agent across frameworks, vendors and organizations without learning its internal implementation.

This guide explains Agent2Agent discovery, task exchange, messages, artifacts and long-running collaboration. It also clarifies the popular search question A2A versus MCP: the protocols solve complementary layers rather than competing for one identical job.

What you will learn

  • A2A focuses on agent-to-agent collaboration while MCP connects an AI application to tools and context.
  • Agent discovery should advertise capabilities without exposing internal prompts, memory or credentials.
  • Tasks need clear identity, lifecycle, authorization and idempotency across organizational boundaries.
  • Multi-agent systems require end-to-end tracing because failures cross several independent services.

Why the Agent2Agent protocol matters

Organizations are building specialized agents for research, security, purchasing, support and operations. Custom point-to-point integration between every pair creates brittle contracts and vendor lock-in.

A2A provides a common collaboration pattern. A coordinating agent can delegate a bounded task to a specialist, receive progress and consume an artifact without controlling how that specialist reasons internally.

Agent discovery and capability cards

Discovery lets a client learn an agent identity, endpoint, supported capabilities and authentication requirements. Publish enough information for safe selection, but do not expose system prompts, private tools or internal network details.

Treat capability descriptions as an API contract. Version them, test compatibility and review changes. A misleading capability description can route sensitive work to an inappropriate agent.

Tasks, messages and artifacts

A task represents a unit of work with a lifecycle. Messages carry communication, while artifacts represent outputs such as reports, files or structured results. Long-running tasks need progress, cancellation and resumable status.

Assign stable task and correlation identifiers. Use idempotency keys so network retries do not create duplicate purchases, tickets or cloud changes. Define expiration and cleanup for abandoned work.

A2A versus MCP

MCP gives an AI host a standard way to use tools, resources and prompts. A2A provides a way for independent agents to collaborate as peers. One specialist agent can itself use several MCP servers while participating in an A2A workflow.

Choose MCP when connecting a host to a database, source-control system or business tool. Choose A2A when delegating a goal to another autonomous service with its own workflow and expertise.

Identity, authorization and trust

Authenticate every participating service and propagate an end-user or workload identity when required. Authorization must consider the calling agent, represented user, task type, data classification and requested side effect.

Do not accept a claim of user approval inside a generated message. Use signed or independently verifiable authorization context, narrow scopes and confirmation for high-impact operations.

Operating multi-agent systems

Capture a distributed trace across coordinator, specialist agents, tools and external systems. Record sanitized messages, task state, version, authorization decisions, latency, cost and artifact lineage.

Test partial failure: an agent timeout, incompatible schema, duplicated callback, revoked credential or unavailable dependency. Set delegation depth and cost limits so agents cannot recursively create an uncontrolled workload.

Production checklist

  • Publish versioned capability contracts without internal secrets.
  • Authenticate agents and authorize each task plus side effect.
  • Use stable task IDs, idempotency, cancellation and expiration.
  • Propagate trace context across agents, tools and artifacts.
  • Limit delegation depth, time, cost and external actions.
  • Test partial failure and compatibility before production federation.

Frequently asked questions

What does A2A stand for?

A2A means Agent2Agent. It is a protocol pattern for communication and collaboration between independent AI agents.

Is A2A a replacement for MCP?

No. MCP connects a host to tools and context; A2A coordinates peer agents. A production agent can use both.

Should agents share their chain of thought?

No. Collaboration should exchange necessary messages, evidence and artifacts, not private hidden reasoning or unrelated internal context.

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Authoritative references