A2A Fans 
Introduction
In the current space of artificial intelligence, the majority of AI agents primarily engage in communication with either tools or human users. For instance, a coding agent interacts with a file system to execute commands, a support agent retrieves information from a Customer Relationship Management (CRM) system to assist clients, and a chatbot responds to inquiries posed by users. While these interactions are undoubtedly beneficial, they do not encompass the complete scope of collaborative work that can occur among multiple agents.
When one AI agent requires the assistance of another agent, especially one that has been developed by a different team, operates on a distinct infrastructure, and utilizes a separate framework, there is often a lack of shared memory and a common toolset to rely upon. This situation creates a need for a standardized method through which agents can discover one another, articulate their capabilities, transfer tasks, monitor progress, and ultimately return results in a coherent manner.
This is precisely the purpose of the Agent2Agent (A2A) Protocol. First introduced by Google in April 2025, the A2A Protocol has since evolved into an open standard under the auspices of the Linux Foundation. It establishes a framework for independent agents to communicate effectively without revealing their internal states, prompts, or the specific tools they utilize. This comprehensive guide aims to elucidate the mechanisms by which agents communicate under the A2A Protocol. It covers essential aspects such as discovery processes, task management, message formats, transport methods, and trust frameworks, and delineates the boundaries of the protocol's functionality.
Key Takeaways
A2A standardizes agent-to-agent collaboration, not agent-to-tool access.
Agents discover peers through Agent Cards, usually at a well-known URL.
Work is organized as stateful Tasks with a defined lifecycle.
Communication is message-based; outputs often return as Artifacts.
Remote agents stay opaque: no shared memory, prompts, or internal tools.
MCP and A2A solve different layers and are designed to work together.
Why Agents Need a Shared Language
Inside one product, developers can hard-wire sub-agents together. Across teams and vendors, that approach collapses. Every new partnership becomes custom integration work. Capability claims are informal. Long-running work has no standard status model. Security and identity are improvised.
A2A exists to reduce that friction. It gives agents a shared interaction model so a client agent can:
Discover what another agent offers
Authenticate as required
Send work as structured messages
Track a task through states such as working, input-required, completed, or failed
Receive results without needing access to the other agent’s internals
The design goal is interoperability between opaque systems, not a single mega-agent that owns every skill.
The Core Idea: Opaque Collaboration
The most important A2A principle is opacity.
When Agent A delegates to Agent B:
B does not see A’s memory
B does not see A’s system prompt
B does not call A’s private tools
A does not control B’s internal reasoning
They cooperate through declared capabilities and the messages they exchange. That is closer to how independent services collaborate than to how functions share a call stack.
This is also why A2A is not the same as “calling another agent as a tool.” A tool call is usually short, local, or tightly integrated and context-rich for the caller. A2A delegation can be long-running, cross-network, and deliberately limited in shared context.
How Discovery Works: Agent Cards
Before agents talk, they need to find each other.
An Agent Card is a public JSON document that acts like a digital business card. It typically advertises:
Name, description, and version
Skills and capability summary
Service endpoint / supported interfaces
Input and output modes
Authentication and security requirements
Optional signatures for stronger authenticity checks
The common discovery pattern is a well-known path, such as:
https://example-agent.com/.well-known/agent-card.json
A client agent fetches the card, checks whether the skills match the need, reviews auth requirements, and only then starts a task. Registries and organizational directories can also index cards, but the card format itself is the baseline discovery unit.
Unsigned cards are easy to publish and easy to spoof. Signed cards and proper authentication improve trust, but operational reputation still comes from real task outcomes over time.
How Work Is Structured: Tasks, Messages, and Artifacts
A2A is task-oriented.
Task: The fundamental unit of work. A task has an ID, a context ID, a status, optional message history, and optional artifacts. It moves through a lifecycle instead of existing as a single fire-and-forget call.
Common states include:
submitted
working
input-required
auth-required
completed
failed
canceled
rejected
Message: A communication turn between client and remote agent. Messages contain one or more parts, which may hold text, files, or structured data.
Artifact: The output produced by a task, documents, structured results, files, or other deliverables the client can use next.
This model matters because real collaboration is rarely one shot. An agent may need more input, may stream progress, or may return a refined artifact after several turns.
How Agents Actually Exchange Messages
At the protocol level, A2A commonly uses JSON-RPC-style methods over HTTP, with additional bindings such as streaming via Server-Sent Events and, in broader deployments, other supported transports.
A simplified conversation looks like this:
Client agent fetches the remote Agent Card.
Client authenticates according to the card’s security scheme.
Client sends a message that creates or continues a task.
Remote agent accepts the work, asks for input, or returns a result.
Client polls, streams, or receives push updates until the task reaches a terminal state.
Client consumes artifacts and continues its own plan.
From the outside, it feels like structured delegation. Under the hood, it is standard web messaging plus a shared task state machine.
A Concrete Example
Imagine a planner agent receives: “Prepare a competitor brief on three companies and return a sourced summary.”
The planner uses its own tools for local context.
It discovers a research specialist through that agent’s Agent Card.
It sends an A2A task with the brief, constraints, and required output format.
The research agent works in its own environment, possibly using MCP servers for search and document access.
If sources are ambiguous, it returns
input-required.When finished, it returns artifacts: notes, source list, and structured findings.
The planner agent drafts the final brief, still without ever seeing the research agent’s private memory or prompts.
That is agent-to-agent talk in practice: capability discovery, task hand-off, status, and results, without merging the two systems into one.
A2A vs MCP: Different Conversations
People often confuse the two protocols.
MCP is how an agent talks to tools and data sources: files, databases, APIs, browsers.
A2A is how an agent talks to another agent.
A production design usually needs both:
Each agent uses MCP to equip itself.
Agents use A2A to delegate and collaborate across boundaries.
Official guidance treats them as complementary layers, not competitors.
Interaction Patterns A2A Supports
A2A is built for more than quick Q&A between agents:
Synchronous request/response for short tasks
Streaming updates for progress on longer work
Asynchronous / push-style follow-up for work that continues after the client steps away
Multi-turn refinement when more input or revision is needed
That flexibility is why the task object matters. Collaboration is often a process, not a single function return value.
Security and Trust in Agent Conversations
Because agents can act, trust is part of communication design.
Important pieces include:
Declared authentication schemes on the Agent Card
Scoped credentials rather than embedded static secrets
Optional signed Agent Cards for authenticity and integrity
Clear permission boundaries on what a remote agent is allowed to do
Logging of task IDs, statuses, and artifacts for audit
A protocol can standardize the handshake. It cannot replace operational judgment. A valid card does not guarantee high-quality work. Verification still comes from task outcomes, monitoring, and policy controls.
Where Platforms Fit
Protocol-level communication solves discovery and message exchange. Many teams still need higher-level workflow pieces: continuous task supply, acceptance criteria, delivery records, and settlement paths.
Platforms that help agents enter real task loops sit above the protocol layer. They may use A2A-style collaboration ideas while adding operational machinery for matching, review, and history. In that sense, agent marketplaces and task platforms are applications of the same underlying problem A2A targets: how independent agents participate in shared work without becoming one system.
Benefits
Framework and vendor independence
Cleaner specialization across agent teams
Reduced custom integration between partner agents
Support for long-running, multi-turn work
Preserved opacity for proprietary logic
A standard substrate for multi-agent systems that span organizations
Limitations
A2A does not tell an agent how to plan, how to use its own tools, or how to guarantee quality. It does not replace MCP. Discovery at the internet scale still needs registries, ranking, and trust infrastructure beyond a single well-known URL. Poorly written Agent Cards create bad matches. Cross-organization liability, monitoring, and permission design remain hard operational problems.
Most importantly, standard conversation does not equal successful collaboration. Clear task definitions and acceptance standards still decide whether agents produce usable results.
Best Practices
Publish accurate, narrowly scoped Agent Cards. Prefer signed cards and strong auth for production or external exposure. Design tasks with explicit inputs, outputs, and failure handling. Support input-required instead of failing silently. Log every inter-agent task the way you log critical API traffic. Keep high-risk actions behind policy checks or human approval. Use MCP inside each agent for tools; use A2A between agents for delegation. Start with one reliable bilateral integration before building a large agent network.
Future Outlook
A2A is moving from early adoption toward broader production use as multi-agent systems become normal in enterprise workflows. Expect better identity and signature practices, richer registries, stronger observability, and tighter pairing with tool-access standards like MCP. The winning systems will not be the ones with the most agents. They will be the ones with the clearest contracts between agents and the best records of completed work.
Conclusion
A2A is how AI agents talk to each other when they do not share memory, tools, or a single runtime.
They publish Agent Cards to be discovered. They exchange messages to negotiate and direct work. They manage collaboration through stateful tasks. They return artifacts while remaining opaque. That is the practical communication model behind modern multi-agent systems.
If MCP is the toolbox interface, A2A is the collaboration interface. Together, they let specialized agents do real work across boundaries, provided the tasks themselves are clear enough to succeed.
Frequently Asked Questions
- What is the A2A Protocol?
An open standard for communication and collaboration between independent AI agents, covering discovery, task delegation, messaging, and result exchange.
- How do two agents find each other?
Primarily through Agent Cards published at well-known URLs or indexed by registries. The card describes skills, endpoints, and auth requirements.
- What is a task in A2A?
A stateful unit of work with an ID, status lifecycle, message history, and optional artifacts.
- Do agents share memory under A2A?
No. Opacity is intentional. Agents collaborate through messages and declared capabilities, not shared internal state.
- Is A2A the same as MCP?
No. MCP connects an agent to tools and data. A2A connects an agent to other agents.
- What transport does A2A use?
Commonly JSON-RPC over HTTP, with streaming options such as SSE and additional supported bindings depending on the deployment.
- Can A2A handle long-running work?
Yes. Tasks can remain open across multiple turns, stream updates, request more input, and complete asynchronously.
- Does adopting A2A guarantee good multi-agent results?
No. The protocol standardizes communication. Quality still depends on agent design, task clarity, permissions, monitoring, and acceptance criteria.