A2A Protocol Explained

A2A (Agent-to-Agent) model, often referred to as the A2A protocol, isn’t a machine learning model like Gemini or GPT. Instead, it’s a communication and interoperability standard for AI agents. Put simply, it’s a framework that lets autonomous AI agents talk and work with each other in a structured and standardized way regardless of who built them or which underlying tech stack they use. We’ll take a closer look at A2A in this article.

What Is A2A?

A2A stands for Agent-to-Agent Protocol. It’s:

• An open, vendor-neutral protocol initially introduced by Google (early 2025) to address the challenge of AI agents operating in isolation.
• Designed to allow distinct AI agents built by different companies or frameworks (e.g., LangChain, Google ADK, custom platforms) to discover, communicate, delegate tasks and collaborate with one another.
• Similar to how HTTP standardizes communication between browsers and servers, but for AI agents instead of web pages.

Why A2A Matters

Today, many AI assistants or agents can’t easily coordinate:

• Your calendar agent can’t automatically talk to your travel booking agent.
• A customer support agent might not be able to hand off a task to an inventory tracking agent.

A2A standardizes how agents communicate, so they can interoperate natively without custom coding for each pair. For example, if one agent is good at booking flights and another at suggesting local events, A2A lets them share tasks and answers seamlessly like human colleagues passing work between each other.

A2A Protocol

Agent Discovery & The “Agent Card”

The protocol moves away from hard-coded integrations by using a standardized Agent Card (a machine-readable JSON file).

• Discovery Mechanism: Agents often publish these cards at a “well-known” endpoint (e.g., https://agent-domain.com/.well-known/agent.json), allowing other agents to “crawl” and find them like a search engine.
• Metadata Content: The card contains the agent’s Identity (e.g., name, version), Capabilities (e.g., streaming support, push notifications) and Skills (specific functional APIs it exposes, e.g., “Market Analysis” or “Flight Booking”).
• Negotiation: Before a task starts, agents use this card to negotiate the best communication protocol (REST vs. gRPC) and authentication method.

Structured Messaging & Task Lifecycle

Communication is built on JSON-RPC and Server-Sent Events (SSE), moving beyond simple “ask-and-answer” toward long-running collaborative workflows.

• The Task Object: Every request is treated as a stateful Task with a lifecycle:
  • SUBMITTED → WORKING → INPUT_REQUIRED → COMPLETED/FAILED.
• Streaming & Artifacts: For tasks like “Write a 50-page research paper,” the remote agent uses SSE to stream “Progress Updates” to the client. Final results are returned as Artifacts, which can include structured data, files or multi-modal content (audio/video).
• Message Parts: Messages are broken into “Parts” (Text, File or Data), allowing agents to exchange complex context without losing formatting or metadata.

Security, Privacy & The “OBO” Pattern

A2A ensures that agents can collaborate without seeing each other’s “inner thoughts” or proprietary logic.

• On-Behalf-Of (OBO) Pattern: This is critical for trust. When Agent A asks Agent B to do something for you, it uses an OBO flow to pass a scoped token. This proves to Agent B that the request is authorized by the actual user, not just a rogue bot.
• Opacity: The “Remote Agent” acts as a black box. It only exposes its inputs and outputs. Its internal prompts, training data and chain-of-thought remain private, protecting corporate intellectual property.
• Authentication: The protocol natively supports enterprise-grade standards including OAuth 2.0, OpenID Connect (OIDC) and mTLS.

A2A vs MCP

While the landscape is crowded, A2A has established a unique “Horizontal” niche:

Protocol	Focus	Analogy
A2A (Agent-to-Agent)	Collaboration: How two independent “minds” work together.	Two colleagues discussing a project.
MCP (Model Context Protocol)	Tools: How an agent uses a database, file or API.	A worker using a specialized tool or hammer.

Tip: In 2026, most advanced systems use a Layered Architecture: They use A2A to coordinate between a “Manager Agent” and a “Specialist Agent”, while those agents use MCP internally to fetch data from their respective databases.

Ecosystem and 2026 Status

The Agentic AI Foundation (AAIF)

Originally as a Google project, A2A has been donated to the Linux Foundation under the newly formed Agentic AI Foundation. This move effectively ended the “protocol wars” by bringing together Google’s A2A, Anthropic’s MCP and OpenAI’s AGENTS.md into a single and neutral governance body.

What This Enables (Real-World Use Cases)

• Cross-Vendor Workflows: A Microsoft 365 agent can seamlessly delegate a specialized research task to a Google Gemini research agent and have the result billed to a Salesforce account.
• Asynchronous Research: An agent can be tasked with a project that takes three days to complete. It will “check in” periodically via A2A updates without the client agent needing to keep a connection open the whole time.
• Autonomous Supply Chains: A retail inventory agent can automatically discover and “interview” multiple supplier agents to find the best price and lead time, negotiating and placing an order entirely via the A2A protocol.

Summary

In conclusion, the A2A (Agent-to-Agent) protocol is the essential “common language” that fulfills the promise of true AI interoperability. Rather than being another standalone model, it serves as the standardized framework that allows autonomous agents, regardless of their developer or underlying tech stack, to communicate and collaborate securely. By bridging the gaps between isolated systems, A2A moves us toward a future where specialized AI agents work as a unified, plug-and-play ecosystem to solve complex, real-world challenges.