AI agents are evolving from isolated tools to collaborative systems. Each works well independently, but connecting them needs custom code that doesn’t transfer across vendors or platforms. Building bridges for only specific agent pairs isn’t scalable.

The Agent2Agent Protocol (A2A) standardizes how AI agents communicate and collaborate across vendors, frameworks, or platforms. It allows agents to discover capabilities, securely exchange information, coordinate tasks, and negotiate result presentation.

By the end of this article, you’ll understand why A2A exists, how it works conceptually, and what problems it solves for developers building multi-agent systems and users relying on AI tools.

What Is the Agent2Agent Protocol?

The Agent2Agent Protocol (A2A) enables AI agents to communicate and collaborate across vendors, frameworks, and platforms. It standardizes capability discovery, information exchange, task coordination, and user experience negotiation.

Think of A2A as a common language for AI agents, enabling them to understand each other’s capabilities and coordinate. Without it, each speaks its own dialect, needing custom translation layers that don’t scale.

Google announced A2A in April 2025, developed with over 50 partners like Atlassian, Box, LangChain, PayPal, SAP, Salesforce, and ServiceNow. It’s an open-source specification under Apache 2.0, promoting community contributions.

Why A2A Exists

AI agents are advancing but remain isolated. Customer service can’t easily transfer to billing, nor can code review agents coordinate with deployment. Each integration needs custom code, tying you to specific vendors and platforms.

The problem affects developers and users differently, but both suffer from the lack of standardization.

The Developer Problem

Before A2A, developers building multi-agent systems faced several challenges:

  • Custom integration code for every agent pair. Connecting two agents required custom communication logic that didn’t work with others.
  • No standard discovery mechanism. Agents couldn’t discover other agents’ capabilities without hardcoded relationships.
  • Vendor lock-in. Once you built integrations for one vendor’s agents, switching meant rewriting everything.
  • Maintenance burden. When agents changed their interfaces, all custom integrations broke.
  • Limited reusability. Integration code for one system couldn’t be shared.

A2A provides a standard protocol enabling agents to communicate without custom code once they speak A2A.

The User Problem

Users face different but related problems:

  • Fragmented experiences. Agents can’t coordinate, so users must manually transfer information.
  • Missing capabilities. Without agent coordination, complex workflows with multiple agents become impossible.
  • Slower innovation. Developers focus on integration code rather than new agent capabilities.
  • Vendor lock-in. Without standards, switching agent platforms leads to lost integrations.

A2A allows agents to coordinate automatically, enabling seamless workflows with task handoffs, shared context, and unified results.

Think of A2A as email protocols for AI agents. Like SMTP and IMAP enable email communication across vendors, A2A allows agents from different vendors to collaborate without knowing their specific platforms.

How A2A Works

A2A employs a discovery and communication model using standard web technologies. Agents publish capabilities, find others, and communicate via structured messages with familiar protocols.

Agent Discovery

Each agent publishes an AgentCard, a JSON document describing its capabilities.

  • Skills and capabilities. What tasks can the agent perform?
  • Supported content types. What formats the agent can handle (text, structured data, audio, video, forms).
  • Operations and endpoints. What actions can the agent perform and how to access them?
  • Authentication requirements. What security mechanisms does the agent require?

Other agents can discover and read AgentCards to understand an agent’s capabilities before communicating. This removes the need to hardcode relationships.

When an agent needs help, it searches for other agents by capability, reads their AgentCards, and chooses one to contact. For example, a customer service agent handling billing might find and review a billing agent’s AgentCard before contacting it. This process is similar to web service registries or APIs using OpenAPI specifications, but for agent communication.

Task-Based Communication

A2A structures communication around tasks, units of work agents collaborate on. Tasks have a lifecycle and can be short (seconds) or long (hours/days).

Agents exchange multi-part messages during tasks, such as text, files, or data, enabling rich context sharing without forcing all into one format. For example, a code review agent might send review results, a summary, and code files when coordinating with a deployment agent.

The task lifecycle includes created, in-progress, completed, and failed states. Agents send status updates to show progress, which is vital for long tasks as others need visibility.

The Protocol Stack

A2A builds on existing standards rather than inventing new ones:

  • HTTP and HTTPS. For transport, use familiar web protocols that integrate with existing infrastructure.
  • Server-Sent Events (SSE). Enabling streaming responses and real-time updates allows agents to get progress info during task execution.
  • JSON-RPC 2.0. Provide a standard way to structure requests and responses for message formatting.

This approach allows A2A to work with existing enterprise systems, load balancers, and security infrastructure using agents that communicate over standard web technologies, without needing special networking or protocols.

The protocol sets standard agent communication methods, like REST APIs for HTTP. Agents send requests, get responses, subscribe, and negotiate via JSON-RPC over HTTP.

Security and Authentication

A2A requires default security. All communication must use HTTPS with TLS, and agents must verify server identities. Authentication occurs at the HTTP layer using OAuth, API keys, or other enterprise schemes.

AgentCards specify supported and required authentication schemes. When agents communicate, they must authenticate using these schemes. This separates security from the protocol, enabling deployment-specific authentication choices.

The security model assumes agents operate in untrusted environments, possibly from different organizations and networks with no shared trust. A2A’s security requirements enable safe communication in these scenarios.

Key Relationships

A2A links to related AI agent concepts.

A2A vs. MCP

Model Context Protocol (MCP) and A2A address different but related issues. MCP standardizes agent-tool interactions (vertical integration), while A2A standardizes agent-agent interactions (horizontal integration).

Think of MCP as the protocol for agents to access external capabilities, and A2A as the protocol for agents to coordinate with each other. A multi-agent system might use both: agents use MCP to access tools and data, and A2A to coordinate with other agents.

Protocols can be integrated: an agent might use MCP to access a database and A2A to share results with another agent. The relationship is complementary.

A2A vs. Direct Agent Integration

Direct integration uses custom code for specific agents. A2A replaces this with a standard protocol, needing A2A-compatible agents but enabling connection to any compatible agent without custom code.

Direct integration offers full control and potentially better performance but needs maintenance and doesn’t scale. A2A sacrifices some control for standardization and ecosystem growth.

The trade-off is like using REST APIs versus custom code: REST APIs are standardized and reusable, but you lose some control over communication. A2A makes the same trade-off for agent communication.

A2A and JSON-RPC

A2A uses JSON-RPC 2.0, a mature, language-independent protocol with standard error handling, compatible with many systems and existing tools.

The relationship is foundational: A2A defines agent communication (application protocol), JSON-RPC details message formatting (message format protocol), and HTTP manages transport. No deep JSON-RPC knowledge is needed for A2A, but knowing A2A builds on standard protocols clarifies its cross-language and cross-system compatibility.

Trade-offs and Limitations

A2A tackles real problems but isn’t suitable for all cases.

Benefits

For developers:

  • Standardization. Once you implement A2A, your agent can communicate with any compatible agent.
  • Discovery. Agents can identify each other and understand capabilities without hardcoded relationships.
  • Reusability. A2A agents can be shared across systems and teams.
  • Language independence. Any language supporting HTTP and JSON can participate.
  • Faster development. Connect with existing agents instead of creating custom integrations.

For users:

  • Seamless workflows. Agents can coordinate automatically for seamless multi-agent experiences.
  • More capabilities. As the A2A ecosystem expands, users gain access to more agent combinations.
  • Faster innovation. Developers add agent capabilities more quickly without needing custom integration code.
  • Interoperability. Use agents from different vendors together.
  • Reduced lock-in. Standard protocols prevent dependency on one vendor’s agent platform.

Costs and Limitations

  • Protocol overhead. JSON-RPC and HTTP increase message formatting and network overhead over direct calls.
  • Network latency. Remote agents add network round-trips not seen with local agents.
  • Agent availability. You need A2A-compatible agents; without them, you require custom integration.
  • Learning curve. Teams must grasp A2A concepts, AgentCards, and task lifecycles.
  • Debugging complexity. Distributed agent communication is harder to debug than local code.
  • Specification maturity. A2A is relatively new (spec version 0.2.2 as of early 2025), so the ecosystem is still developing.

When Not to Use A2A

A2A isn’t always the right choice:

  • Performance-critical paths. For microsecond performance, direct integration is faster.
  • Simple, single-agent systems. If you have only one agent and no coordination, A2A adds unnecessary complexity.
  • Tightly coupled agents. If agents are always deployed together and never change, direct integration might be simpler.
  • Legacy agents without A2A support. You still need custom integration for agents not supporting A2A.

The goal isn’t to use A2A everywhere but where interoperability and ecosystem growth outweigh performance or simplicity.

Common Misconceptions

Several misconceptions about A2A cause confusion:

A2A is only for large, complex systems

A2A suits systems of any size, including small ones with few agents. Its minimal protocol overhead offers benefits like agent discovery and interoperability, even for small systems.

A2A requires complex infrastructure

A2A uses standard HTTP and HTTPS, working with existing web infrastructure. No special networking or distributed systems skills are needed. Many deployments are simple HTTP services.

A2A replaces MCP

A2A doesn’t replace MCP; they solve different problems. MCP handles agent-to-tool communication, A2A handles agent-to-agent communication. Many systems use both.

A2A agents are hard to build

A2A agents are easy to implement if you know HTTP and JSON. They use familiar web tech, and the AgentCard is simple JSON. Building an A2A agent is often easier than a custom integration.

A2A is only for Google’s ecosystem

Google announced A2A, an open-source protocol with an Apache 2.0 license. Its public spec invites community contributions. Over 50 industry partners helped develop it, aiming for cross-vendor interoperability.

A2A requires agents to share memory or context

A2A supports agents without shared memory, tools, or context. It allows unstructured interactions between agents with varied capabilities, communicating via messages, not shared state.

Conclusion

A2A solves real issues for developers and users by removing the need for custom agent integrations, cutting costs, and speeding up development. For users, it offers seamless multi-agent workflows, faster innovation, and a growing ecosystem of interoperable agents.

A2A uses web technologies (HTTP, JSON-RPC, SSE) for agents to discover capabilities and coordinate tasks. It includes AgentCards for discovery, task-based communication with multi-part messages, and security that integrates with existing enterprise infrastructure.

Benefits include standardization, discovery, and ecosystem growth, but A2A isn’t always ideal. Performance-critical paths, simple single-agent systems, and legacy agents may need custom solutions. Knowing when interoperability outweighs performance or simplicity is key to deciding if A2A fits your needs.

Next Steps

Now that you understand A2A and its purpose, here’s what to explore next.

  • Read the specification. The A2A Specification provides complete protocol details and architectural decisions.
  • Explore the GitHub repository. The A2A GitHub Repository includes examples, SDKs, and community resources that illustrate the concepts.
  • Study agent implementations. Existing A2A agents demonstrate how discovery, task coordination, and security work in practice.
  • Check Google’s documentation. Google’s A2A documentation provides additional context about the protocol’s design and ecosystem.

When working with MCP, understanding A2A helps clarify protocol use. Many systems benefit from both: MCP for agent-to-tool, and A2A for agent-to-agent coordination.

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