AI agents working together: why a common protocol matters
AI agents — software systems that autonomously execute tasks on behalf of users — are proliferating. But agents built by different vendors, using different frameworks, have had no standard way to communicate with each other. Google's Agent-to-Agent (A2A) protocol, announced in April 2025 with 50+ technology partners, is designed to change that.
This article explains AI agents from the ground up, walks through how A2A works, and covers what distinguishes it from traditional API integration and Anthropic's MCP protocol.
What is an AI agent?
An AI agent is an AI system that executes tasks autonomously — gathering information, adjusting schedules, coordinating with other services — based on user instructions. Voice assistants and chatbots are common examples. More advanced agents use reasoning, planning, and memory to pursue multi-step goals.
Multi-agent systems — where multiple AI agents collaborate — are increasingly important. Agents with different specializations working together can handle complex tasks that no single agent could manage alone.
What A2A is and how it works
A2A is an open protocol for communication between AI agents. The core purpose: allow agents built by different vendors and on different frameworks to directly exchange information, coordinate, and take action together.
Roles in A2A:
- Client agent: the agent making a request (the initiating side)
- Remote agent: the agent receiving and acting on the request
Agent cards: Each agent publishes its capabilities in a JSON-format self-description called an Agent Card. Other agents use these cards to discover what a given agent can do and how to work with it.
Task lifecycle: Client agents send structured "task" objects to remote agents. As work progresses, agents exchange messages and status updates. When a task completes, the result is delivered as an "artifact" — a defined output object.
Content negotiation: A2A goes beyond simple text exchange. Each message contains "parts" specifying content types — images, documents, form inputs. Agents can negotiate user experience details: "Does the user's environment support image display?" "Is a form required?" This allows agents to coordinate not just task execution but how results are presented to the end user.
Security: Authentication and authorization mechanisms are built into the protocol at the enterprise level.
The overall concept: A2A gives AI agents a common language. Rather than each agent being a silo, A2A creates the infrastructure for a unified ecosystem where agents across products and companies can work together.
How A2A differs from traditional API integration
Traditional API integration (REST, webhooks) involves one system calling predefined endpoints on another. The format and capabilities are fixed in advance. There's no mechanism for dynamic capability discovery or interactive negotiation of response format.
In API-based integration, an agent called via API is treated as a tool — a subordinate resource. A2A treats agents as collaborative peers capable of genuine two-way coordination.
A2A is also built on existing web standards (HTTP, JSON, JSON-RPC, SSE for streaming) — meaning it doesn't require specialized infrastructure and integrates naturally with existing enterprise systems.
A2A vs. Anthropic's MCP: complementary, not competing
MCP (Model Context Protocol), developed by Anthropic, standardizes how AI agents connect to external tools and data sources — providing agents with the context and capabilities they need to execute tasks.
A2A standardizes how agents communicate with each other.
Google's official position: A2A is a complementary open protocol alongside Anthropic's MCP. They serve different roles:
| Protocol | Focus |
|---|---|
| MCP | Agent ↔ Tools/Data |
| A2A | Agent ↔ Agent |
Used together: an agent can use MCP to access external data and APIs while using A2A to collaborate with other specialized agents. Google's Agent Development Kit (ADK) supports both MCP-based external connections and A2A-based agent-to-agent communication.
Business applications
Recruitment automation: The hiring process involves writing job descriptions, candidate searching, interview scheduling, background checks. With A2A:
- Recruiter agent receives instruction: "Find candidates matching these criteria"
- Agent queries a specialized candidate-search remote agent via A2A
- Returns a shortlist to the recruiter
- Recruiter instructs: "Schedule interviews with these candidates"
- Scheduling agent coordinates with interview participants, delegates email communication to a notification agent
- Background check agent handles verification upon offer
What previously required custom integration between separate HR systems becomes a collaborative conversation between agents — each doing what it specializes in.
Customer support: A front-facing agent receives inquiries and identifies intent. It routes to a knowledge-base search agent (via A2A) to retrieve relevant information, then to a response-generation agent to draft the answer, and to a billing or inventory agent if additional data is needed. The user receives an integrated response without the manual hand-offs that currently slow support operations.
The pattern applies broadly: marketing and sales agents coordinating on lead nurturing, IoT device management agents working with analytics agents to optimize factory operations, finance systems agents collaborating with compliance agents.
Developer considerations
Open specification: The A2A draft specification is publicly available. Anyone can contribute. Built on familiar web standards — HTTP, JSON, JSON-RPC, SSE — no specialized infrastructure required.
Security: Authentication and authorization follow OpenAPI-equivalent standards. OAuth 2.0 and API key authentication are supported. Access control can be configured per-agent. HTTPS and message signing are recommended.
Agent Cards: Developers must implement Agent Cards describing their agent's capabilities, input/output formats, and required authentication scopes. Keeping these accurate is the foundation of reliable interoperability.
Task lifecycle: Tasks are stateful objects, not simple API calls. Developers implement start, in-progress, complete, and failure states. Long-running tasks can stream intermediate results. Processes requiring human approval (e.g., waiting for sign-off) require pause/resume logic.
Multimodal content: If your agent produces images, audio, or other non-text content, implement appropriate content type specifications in message parts. A2A is designed to be modality-agnostic and will expand to additional content types over time.
Cross-platform integration: Remote agents may be built in entirely different tech stacks. Test interoperability explicitly. Google's ADK is designed for seamless integration with LangChain, Crew.ai, and standard APIs.
What A2A makes possible
When A2A achieves broad adoption, agents built by different companies on different platforms will interoperate as naturally as web browsers talk to web servers. The analogy is intentional: TCP/IP and HTTP turned fragmented networks into the internet. A2A aims to do the equivalent for AI agents.
Google has announced an AI agent marketplace concept on Google Cloud — where partner agents can be listed, discovered, and purchased for integration into enterprise environments. A2A-compatible agents can be dropped into existing agent ecosystems without custom integration work — the equivalent of an app store for specialized AI capabilities.
Governance benefits follow naturally: standardized protocols mean standardized logging, monitoring, and audit trails. Enterprises can track which agents did what, maintaining the oversight that enterprise AI deployment requires.
The current A2A specification is at draft stage; a production-ready version is planned for 2026. As adoption grows and case studies accumulate, the protocol will mature. The direction is clear: the era of agents working in isolation is ending.
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