A2A 1.0: Why Agent-to-Agent Communication Needs a Real Protocol

Giving one agent another agent’s URL does not create collaboration. The systems still need to discover capabilities, agree on a task, exchange updates, deliver artifacts and handle failure without exposing private memory or implementation details. Agent2Agent, or A2A, is an attempt to standardize that boundary.

The A2A project announced version 1.0 on March 12, 2026 as its first stable, production-ready release. Its central object is not a chat message but a task with an identity, lifecycle and result. An agent can publish an Agent Card describing its capabilities and supported interaction methods. Another system can send work, receive status updates, stream intermediate progress or wait for an asynchronous notification. Outputs are represented as artifacts rather than being trapped inside an informal transcript.

This matters when agents are built by different teams, frameworks or vendors. The receiving agent can remain opaque. It does not have to reveal its internal prompt, model, memory or tools in order to accept a bounded request.

MCP and A2A solve different boundaries

MCP commonly connects an AI application to tools and data. A2A connects one agentic application to another agentic application. The first expands what an agent can reach; the second allows responsibility for a task to cross a system boundary.

They can be used together. A coordinator may delegate a research task over A2A, while the specialist agent uses MCP tools to search approved repositories. But combining protocols does not merge their trust models. The coordinator still needs to know what it may disclose to the specialist, which outputs it will accept and who remains accountable for the final action.

More agents do not automatically mean more intelligence

Multi-agent designs add value when a second system contributes information, capability or independent verification that one agent cannot obtain alone. They add overhead when roles merely repeat the same reasoning with additional tokens and latency.

The failure modes are familiar from distributed work: incomplete handoffs, conflicting updates, duplicated effort, timeouts and responsibility gaps. A remote specialist can return a polished artifact that is wrong. A manager agent can accept it because the wording sounds confident. Two agents can call each other repeatedly without making progress.

The answer is not a longer coordination prompt. It is a stronger contract:

• define the input package and allowed disclosures;
• give the task a clear completion condition;
• require structured status and error states;
• set time, cost and retry limits;
• validate the artifact independently;
• preserve the initiating human or service as the accountable owner.

A practical adoption test

Before introducing A2A, run the same workload with one agent and ordinary tools. If the single-agent design can complete the job reliably, a second agent may be unnecessary. If specialization is justified, isolate one boundary first: for example, research, code review or media generation. Measure whether the handoff improves quality or throughput enough to compensate for orchestration cost.

Interoperability is useful because it reduces the cost of crossing boundaries. It does not make those boundaries safe by default. Treat Agent Cards as discoverable interfaces, task histories as potentially sensitive records and remote artifacts as untrusted until validated.

For the tool layer, read MCP Is Becoming Infrastructure. For permissions and recovery, continue with AI Agent Safety.

Primary sources

• A2A Protocol v1.0 announcement
• A2A official specification
• Google Cloud donation of A2A to the Linux Foundation

Source and adaptation note: This article also draws on general multi-agent design concepts in AI Agents in Depth: Design Principles and Engineering Practice by Bojie Li and contributors, distributed under Apache License 2.0. The text, organization, examples and conclusions here were independently rewritten and expanded for Stariy.com.