Agent-to-Agent Communication Protocols

Agent-to-agent (A2A) communication refers to the structured exchange of messages, tasks, and results between autonomous AI agents. As AI systems grow more agentic — capable of planning, using tools, and running for extended periods — the question of how multiple agents coordinate has moved from research to engineering practice.

A2A protocols define the language agents use to communicate: how they advertise capabilities, request work, return results, handle errors, and establish trust.

Why A2A Communication Matters

Individual AI agents are limited in what they can accomplish alone. Multi-agent systems offer:

• Specialization: A routing agent delegates sub-tasks to specialized experts (coder, researcher, reviewer).
• Parallelism: Multiple agents work simultaneously on independent tasks.
• Scale: Long-horizon tasks are broken into manageable sub-tasks.
• Verification: One agent generates, another verifies.
• Resource isolation: Security-sensitive operations are isolated to sandboxed agents.

For these benefits to be realized, agents need a common protocol — just as web services need HTTP.

The Google Agent-to-Agent (A2A) Protocol

A2A (Google, 2025) is an open protocol for agent interoperability, allowing agents built on different frameworks and platforms to communicate through a standard API.

Key Concepts

Agent Card: A JSON document published at /.well-known/agent.json that describes an agent’s capabilities, skills, input/output formats, and authentication requirements — analogous to an OpenAPI spec for agents.

{
  "name": "Research Agent",
  "description": "Searches the web and synthesizes information on a topic",
  "skills": [
    {
      "id": "web_research",
      "name": "Web Research",
      "description": "Search the web for information on a topic",
      "inputModes": ["text"],
      "outputModes": ["text", "structured"]
    }
  ],
  "authentication": {
    "schemes": ["Bearer"]
  }
}

Task: The fundamental unit of work. A task has a lifecycle: submitted → working → completed/failed.

Message: A structured exchange within a task. Messages carry Parts — text, files, structured data, or embedded UI components.

Artifact: The output of a completed task (a file, a data structure, a report).

A2A Communication Flow

Client Agent                    Remote Agent
    |                                |
    |--- POST /tasks/send ---------->|
    |    (Task with initial message) |
    |                                |
    |<-- 200 OK (task accepted) -----|
    |                                |
    |--- GET /tasks/{id} ----------->|
    |<-- Task status: "working" -----|
    |                                |
    |<-- SSE: task/artifact-update --|
    |    (streaming partial result)  |
    |                                |
    |<-- SSE: task/completed --------|
    |    (final result)             |

A2A supports both synchronous and streaming (SSE-based) communication, and push notifications for long-running tasks.

Trust and Security in A2A

A2A explicitly addresses the trust problem between agents:

• Authentication: Agents authenticate to each other using standard web mechanisms (OAuth 2.0, API keys, JWT).
• Authorization: The agent card declares what operations are permitted.
• Capability verification: Clients can inspect an agent’s card before delegating sensitive tasks.
• Audit trail: All task exchanges are logged for compliance and debugging.

The protocol intentionally does not assume agents trust each other implicitly — each agent independently validates inputs and enforces its own safety policies.

Model Context Protocol (MCP)

MCP (Anthropic, 2024) is a complementary protocol focused on how an AI agent accesses tools and resources — databases, APIs, file systems — rather than how agents communicate with each other.

In a production system, an orchestrator agent might:

1. Use A2A to delegate research to a specialist agent.
2. Use MCP to read from a database and write to a file system.

Message Formats and Structures

OpenAI Agents SDK Message Format

The OpenAI Agents SDK uses a thread-based message model:

from agents import Agent, Runner

research_agent = Agent(
    name="Research Agent",
    instructions="Search the web and summarize findings.",
    tools=[web_search, fetch_url],
)

result = await Runner.run(
    research_agent,
    "What are the key findings from recent AI safety research?"
)

LangChain/LangGraph Agent Messages

LangGraph uses typed message objects passed between graph nodes:

from langchain_core.messages import HumanMessage, AIMessage, ToolMessage

messages = [
    HumanMessage(content="Analyze this dataset"),
    AIMessage(content="I'll start by loading the data", tool_calls=[...]),
    ToolMessage(tool_call_id="...", content="Dataset loaded: 1000 rows"),
]

AutoGen Agent Communication

Microsoft AutoGen implements agent conversation as a sequence of messages between named agents:

user_proxy.initiate_chat(
    assistant,
    message="Write and run a Python script to analyze sales data.",
)

AutoGen agents communicate through natural language messages in a shared conversation context, with the orchestrator deciding when to terminate.

Trust Hierarchies in Multi-Agent Systems

Multi-agent systems require trust hierarchies to prevent prompt injection and unauthorized delegation:

Tier 0 — Orchestrator: Trusted system agent with full permissions. Creates and delegates to sub-agents.

Tier 1 — Specialist Agents: Trusted for specific domains. Cannot spawn new agents without orchestrator approval.

Tier 2 — External Agents: Third-party agents accessed via A2A. Operate in sandboxed contexts with limited permissions.

Tier 3 — Tool Results: Outputs from external APIs and web content. Treated as untrusted until validated.

A key security principle: an agent should never inherit higher privileges than it was granted, even when orchestrated by a more privileged agent.

Emerging Standards and Ecosystem

As of 2025, A2A and MCP are gaining traction as cross-framework interoperability standards, with major AI providers beginning to announce compatibility.

Challenges in A2A Systems

• Latency accumulation: Each agent-to-agent hop adds latency. Deep delegation trees can produce unacceptable response times.
• Error propagation: A failure in a downstream agent must be gracefully handled and communicated back through the chain.
• Context loss: Passing tasks between agents often strips conversational context. Agents must receive sufficient context to complete their sub-task independently.
• Infinite loops: Two agents can instruct each other to act indefinitely. Cycle detection and max-step limits are essential.
• Billing and cost attribution: In multi-agent systems, tracking which agent generated which LLM cost requires careful instrumentation.

Further Reading

• A2A Protocol Specification — Google, 2025
• Model Context Protocol — Anthropic, 2024
• OpenAI Agents SDK Documentation
• Building Multi-Agent Systems with LangGraph