Agent-to-Agent Protocol (A2A)

The Agent-to-Agent Protocol (A2A) is an open standard developed by Google that enables AI agents to discover, communicate with, and collaborate with other agents. While MCP connects agents to tools, A2A connects agents to each other — enabling true multi-agent systems.

• Agent interoperability: A2A provides a common language for agents built on different platforms, frameworks, or LLMs to work together seamlessly.
• Dynamic discovery: Agents can find and evaluate other agents at runtime through standardized capability descriptions called "Agent Cards."
• Task delegation: A2A defines how one agent can assign work to another, track progress, and receive results — enabling complex collaborative workflows.
• Enterprise backing: Launched in April 2025 with support from over 50 technology partners including Salesforce, SAP, ServiceNow, MongoDB, and Atlassian.
• Complementary to MCP: A2A handles agent-to-agent communication while MCP handles agent-to-tool communication — together they form a complete connectivity stack.

A2A is a specification for how autonomous AI agents communicate, delegate work, and collaborate in multi-agent environments. If MCP is "USB-C for AI tools," A2A is "TCP/IP for AI agents" — the networking layer that lets agents find and talk to each other.

The protocol addresses a fundamental challenge: as AI agents proliferate across enterprises, they need to work together. A customer service agent might need to delegate order lookups to a commerce agent, which might need to consult a shipping agent. Without a standard protocol, every agent-to-agent integration requires custom code.

A2A defines three core concepts:

• Agent Cards: JSON documents that describe an agent's capabilities, input/output formats, and authentication requirements — like a machine-readable résumé
• Tasks: Units of work that one agent delegates to another, with defined lifecycles (submitted, working, completed, failed)
• Messages: The communication payloads exchanged during task execution, supporting text, files, structured data, and streaming updates

The protocol is transport-agnostic, supporting HTTP, WebSockets, and other communication channels. It includes built-in support for long-running tasks, partial results, and human-in-the-loop workflows.

Agent Discovery

Before agents can collaborate, they need to find each other. A2A supports multiple discovery mechanisms:

• Static configuration: Agents are configured with known endpoints at deployment time
• Registry lookup: Agents query centralized or federated registries to find agents with needed capabilities
• DNS-based discovery: Agent Cards can be published at well-known URLs, similar to robots.txt or security.txt

When an agent discovers another agent, it fetches the Agent Card to understand what the remote agent can do, what inputs it accepts, and how to authenticate.

Task Lifecycle

A2A defines a standard task flow:

1. Submit: Client agent sends a task request with input data
2. Accept: Server agent acknowledges and returns a task ID
3. Work: Server agent processes the task, optionally sending progress updates
4. Complete: Server agent returns results, or reports failure with error details

Tasks can be synchronous (wait for completion) or asynchronous (poll for status). The protocol supports streaming partial results for long-running tasks — essential for generative workflows where incremental output is valuable.

Multi-Agent Orchestration Patterns

A2A enables several collaboration patterns:

• Delegation: One agent hands off a sub-task to a specialist agent
• Consultation: An agent queries another for information without delegating control
• Collaboration: Multiple agents work on shared artifacts, coordinating through A2A messages
• Hierarchy: Manager agents decompose work and distribute it across worker agents

Google's internal benchmarks show multi-agent systems using A2A complete complex enterprise workflows 45% faster than monolithic agents attempting the same tasks.

Security and Trust

A2A integrates enterprise security requirements:

• Authentication: Supports OAuth 2.0, API keys, and custom schemes defined in Agent Cards
• Authorization: Agents can specify required permissions for different operations
• Audit trails: All task exchanges can be logged for compliance and debugging

Capability verification: Agents can validate that remote agents have claimed capabilities before delegating sensitive work

1. Break Down Agent Silos

Enterprises deploying AI often end up with isolated agents — a customer service agent that can't access inventory data, a coding agent that can't query the issue tracker. A2A enables these agents to collaborate, creating unified experiences from specialized components.

2. Enable Best-of-Breed Agent Selection

Different tasks need different capabilities. With A2A, organizations can use the best agent for each job — Claude for analysis, Gemini for search, specialized fine-tuned models for domain tasks — and compose them into coherent workflows. No vendor lock-in.

3. Scalable Agent Architectures

A2A's task-based model naturally supports horizontal scaling. Heavy workloads can be distributed across multiple agent instances. Specialist agents can be scaled independently based on demand. The protocol handles coordination.

4. Faster Development Through Composition

Building complex AI applications becomes assembly rather than construction. Developers can compose existing agents with proven capabilities instead of building everything from scratch. Early adopters report 50% reduction in development time for multi-capability AI applications.

Coordination Complexity

Multi-agent systems introduce distributed systems challenges: partial failures, inconsistent state, race conditions, deadlocks. Debugging issues across agent boundaries is harder than debugging monolithic applications. Teams need distributed systems expertise to build reliable A2A deployments.

Trust and Security Across Boundaries

When agents delegate to other agents, trust chains become complex. A malicious or compromised agent could poison results, leak sensitive data, or exhaust resources. Organizations need robust policies for which agents can communicate with which, and what data can flow between them.

Latency and Performance

Every A2A call adds network latency and serialization overhead. Deeply nested agent hierarchies can accumulate significant delay. Architects must balance the benefits of agent specialization against the costs of coordination overhead.

Early-Stage Ecosystem

A2A launched in April 2025 and is still maturing. The specification may evolve, tooling is limited, and best practices are still emerging. Organizations adopting A2A should expect some friction and plan for specification changes.

A2A represents the next evolution in AI system architecture — from single agents to agent ecosystems. Just as microservices transformed how we build applications, multi-agent systems will transform how we build AI.

The parallel to distributed computing is instructive. Early computing was monolithic; networks enabled distributed systems; standardized protocols (TCP/IP, HTTP, REST) made the internet possible. AI is following the same trajectory: early AI was monolithic (single models, single prompts); agentic AI enabled autonomous behavior; protocols like A2A will enable an "internet of agents."

For engineering teams, A2A means thinking about agents as services — discoverable, composable, independently deployable. The skills that made developers effective with microservices and APIs will transfer directly. Those who master multi-agent architecture early will shape how enterprise AI evolves.

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