Inside A2A: How Google’s Agent2Agent Protocol Actually Works

The rise of agentic workflows demands more than isolated function calls or monolithic orchestration layers, it requires agents that can dynamically discover, delegate, and collaborate. Google’s Agent2Agent Protocol (A2A) introduces a standardized way for AI agents to interoperate, regardless of how or where they’re built. Much like HTTP enabled a web of interconnected applications, the A2A protocol promises a fabric where autonomous agents function as modular, interoperable services.

In this blog, we’ll unpack how A2A actually works under the hood, from agent discovery and task orchestration to message-passing and opaque execution. For developers building with or around agent frameworks, this deep dive offers a clear technical blueprint of what Agent2Agent (A2A) is solving, and how.

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What Is the A2A (Agent2Agent) Protocol and How It Actually Works

The Agent2Agent (A2A) protocol is Google's emerging specification for enabling structured, scalable communication between autonomous AI agents. As modern AI ecosystems shift toward decentralized, multi-agent systems capable of handling distributed tasks across domains, the A2A protocol provides a standardized messaging framework that defines how agents interact, share state, delegate subtasks, and reach consensus, all without hard-coding integrations or relying on brittle custom APIs.

At its core, A2A serves as a low-latency, high-fidelity agent orchestration layer, abstracting away the transport, negotiation, and task alignment challenges typical in multi-agent systems. Unlike point-to-point communication models, where agents need bespoke logic to interpret each other’s intent or schema, the Agent2Agent protocol enforces a shared grammar and ontology. This allows agents built by entirely different teams, or even trained on different foundation models, to interoperate seamlessly.

While comparable in spirit to frameworks like Anthropic’s Model Context Protocol (MCP), which unifies tool and memory access, Google’s Agent2Agent Protocol extends beyond context injection. It supports bidirectional intent negotiation, sub-agent spawning, stateful conversation threading, and persistent identity across agent hops.

In short, Agent2Agent is not just a communication bridge, it's an architectural layer that treats AI agents as network-addressable actors with contracts, capabilities, and composable behaviors. As we'll see, this unlocks powerful new patterns in distributed AI design.

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The Context Behind Google’s A2A Protocol

Since the release of GPT-3.5 in late 2022, the landscape of AI system design has shifted rapidly from monolithic, chat-based interfaces to modular, composable agent architectures. This transformation, fueled by the commercial viability of large language models (LLMs), catalyzed a new wave of development where LLMs were no longer isolated responders but autonomous actors embedded in broader application stacks.

Initial attempts to augment LLMs with external capabilities relied heavily on function-calling APIs, enabling one-to-one integration patterns where models could invoke predefined endpoints, a pattern exemplified by features like GPT Actions. However, this model quickly hit architectural limitations: each vendor (OpenAI, Google, Anthropic, etc.) and implementer introduced non-interoperable, proprietary interfaces, creating a fractured agent ecosystem.

To address the combinatorial integration explosion (the classic NxM problem, where N agents must interface with M tools or APIs), frameworks like the Model Context Protocol (MCP) emerged. MCP proposed a vendor-neutral standard for injecting tool access and memory into the model’s execution context, making it easier to develop agent workflows without rewriting glue code for each tool-model pair. MCP effectively tackled tool-level context unification, which benefits both user experience (less hallucination, more relevance) and developer efficiency (fewer bespoke wrappers).

However, MCP stops short at inter-agent communication. It’s designed to facilitate agent-to-tool interoperability, not agent-to-agent orchestration. And this is where Google’s Agent2Agent Protocol (A2A) enters, not as a replacement, but as a complementary communication substrate that allows autonomous agents to negotiate, delegate, and synchronize across task boundaries.

The A2A protocol redefines what it means for AI agents to collaborate. Rather than requiring developers to manually engineer bridges between agent runtimes, A2A offers a first-class messaging and capability negotiation layer, abstracting agent identity, session state, task goals, and protocol grammar into a unified envelope.

It’s important to note that the term "agent" itself remains semantically fluid. Some frameworks differentiate between lightweight task runners and "agentic AI" (agents with persistent memory, long-term goals, and reasoning loops), while others, like Amazon's Bedrock and Google’s own Agent2Agent implementations, emphasize autonomy and goal fulfillment as core characteristics. Regardless of these nuances, the A2A protocol is designed to operate at this emergent boundary, where agents aren't just reactive, but act as proactive collaborators in distributed cognitive workflows.

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What Exactly Is Google’s Agent2Agent (A2A) Protocol?

The Agent2Agent (A2A) protocol is Google’s open specification for enabling seamless interoperability between autonomous AI agents, a foundational layer designed to support structured communication, coordination, and delegation across diverse agent frameworks and execution environments.

At its core, A2A functions as a vendor-neutral, transport-agnostic agent communication bus, allowing agents built on disparate stacks, including LangChain, AutoGen, CrewAI, and LlamaIndex, to exchange structured messages, negotiate intents, share capabilities, and invoke each other without custom glue code. Rather than enforcing brittle, point-to-point integrations, A2A abstracts away agent implementation differences, effectively serving as a universal interlingua for multi-agent ecosystems.

Unveiled at Google Cloud Next in April 2025, the A2A protocol is the result of a collaborative, multi-stakeholder initiative involving over 50 technology partners. Enterprise leaders like Atlassian, Salesforce, SAP, and MongoDB are among the early adopters, positioning A2A not as a closed Google-only project, but as a cross-industry standard for building interoperable AI agent networks.

From a systems design perspective, Google’s Agent2Agent protocol treats each agent as a network-addressable service that exposes capabilities through declarative contracts, similar to how RESTful APIs expose endpoints via HTTP. In this analogy, A2A is to agents what HTTP is to web services: a standardized layer that transports intents, plans, and metadata instead of hypertext.

What sets A2A apart is its comprehensive specification, which includes:

• Typed message formats for actions, responses, errors, and system metadata
• Session state encoding to support long-lived, multi-hop agent workflows
• Dynamic capability advertisements, so agents can introspect and invoke each other's skills
• Security primitives, enabling authentication, identity management, and access control across agent networks

Whereas the Model Context Protocol (MCP) addresses NxM tool-model integrations by injecting context into a single model, the A2A protocol targets the NxN challenge of inter-agent communication, eliminating the need to hardcode compatibility between every pair of agents. By adhering to A2A-compliant interfaces, developers gain a common semantic and syntactic layer for building multi-agent workflows that are modular, interoperable, and reusable.

In essence, Agent2Agent turns AI agents into composable infrastructure components, cognitive microservices that can be orchestrated dynamically based on their declared goals and capabilities. This architectural shift dramatically reduces friction in building distributed AI systems and unlocks new possibilities for adaptive, goal-driven automation at scale.

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Core Capabilities of the Agent2Agent (A2A) Protocol

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Core Capabilities of the Agent2Agent (A2A) Protocol

At its foundation, the A2A protocol is built to support modular, interoperable agent networks, where agents function as loosely coupled services capable of real-time collaboration, dynamic goal delegation, and adaptive response rendering. To understand how Google’s Agent2Agent protocol enables this vision, it’s helpful to define its two primary agent roles:

• A2A Client (Client Agent): The initiating agent or orchestrator. It decomposes high-level goals into subgoals and delegates tasks to downstream agents using A2A-compliant interfaces.
• A2A Server (Remote Agent): A target agent that exposes an HTTP-accessible endpoint and adheres to the A2A protocol specification. It advertises its capabilities, receives task requests, and returns structured responses.

With this client-server agent architecture in place, the Agent2Agent (A2A) protocol enables four core capabilities:

1. Capability Discovery

"What can you do?"

At the heart of the A2A protocol is a standardized mechanism for capability discovery, where remote agents publish an Agent Card, a structured JSON document that describes their available functions. This card includes:

• Supported task types
• Input/output schemas
• Required authentication
• Service-level metadata (e.g., latency or reliability hints)

This enables dynamic agent selection: clients can query, filter, and invoke agents programmatically without prior knowledge of their internal implementation. Think of it as the OpenAPI for AI agents, where instead of REST endpoints, clients explore semantic capabilities, a major step toward interoperable multi-agent systems.

2. Task Management

"What are you doing, and how far along are you?"

The A2A protocol introduces a strongly typed task object model for managing and coordinating agent workflows. Each task includes:

• A globally unique task ID
• Status enum (e.g., Pending, InProgress, Completed, Errored)
• Associated agents
• Retry and timeout strategies
• Optional hierarchical relationships (for nested or dependent tasks)

Unlike traditional polling-based systems, A2A supports asynchronous updates, allowing remote agents to push real-time progress notifications. This makes A2A a native fit for long-running or multi-hop workflows, where agents need to reason and act over extended timelines without external orchestration glue.

3. Collaboration

"What’s the context of our conversation?"

True to the Agent2Agent philosophy, A2A facilitates rich, bidirectional communication via structured messages that include:

• Threaded context (e.g., thread ID, task ID)
• Composable payload parts (e.g., JSON plans, code snippets, UI specs)
• Sender/receiver identity for traceability
• Context scaffolding for chaining or rejection

This structured messaging layer allows for intent-aware, multi-turn exchanges between agents, not just stateless API calls. Agents can reason with shared state, respond with intermediate results, and negotiate over incomplete inputs, enabling context-driven collaboration across agent chains.

4. User Experience Negotiation

"How should the output be presented to the user?"

A standout feature of the A2A protocol is its built-in support for UI-aware message construction. Using message parts annotated with MIME types, agents can return content blocks optimized for different frontends, such as:

• HTML fragments
• Markdown or code blocks
• JSON schema-based interactive forms
• Embedded media (e.g., video, images, iframes)

This allows presentation adaptation based on client capabilities, whether it's a mobile app, browser plugin, or terminal interface. By integrating UI semantics into the communication protocol, Agent2Agent helps bridge backend cognitive logic with frontend UX, a critical enabler for production-grade multi-agent deployments.

Together, these four capabilities establish the A2A protocol as far more than a messaging layer. It is a decentralized execution framework for agent-based systems, enabling discovery, delegation, coordination, and cross-platform presentation of task outputs with minimal developer overhead. Google’s Agent2Agent protocol transforms isolated agents into cooperative, composable AI services, the foundation of next-generation multi-agent architectures.

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How A2A Works with AI Agents

The Agent2Agent (A2A) protocol is Google’s proposed standard for enabling autonomous AI agents to interact as modular, HTTP-native services. Much like HTTP standardized web communication, A2A aims to provide a universal protocol for agent interoperability, critical in an emerging ecosystem of heterogeneous, goal-driven agents operating across platforms, vendors, and levels of autonomy.

A2A is not just a communication layer, it’s a full execution model for dynamic discovery, task delegation, multi-turn collaboration, and presentation negotiation between agents.

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Core Building Blocks of A2A

1. Agent Card

• What it is: A machine-readable JSON descriptor, usually hosted at a well-known path like /.well-known/agent.json.
  
  
• Why it matters: Agent Cards act as semantic introspection tools. They expose an agent’s declared capabilities, endpoint details, authentication requirements, and accepted input/output schemas.
  
  
• Analogy: Just as OpenAPI specs tell you how to interact with an API, an Agent Card offers the contractual surface area of an AI agent, enabling client agents to query and select remote agents programmatically.
  

‍2. A2A Server (Remote Agent)

• What it is: An agent that exposes A2A-compatible endpoints, responding to task requests from client agents.
  
  
• Why it matters: These agents can be anything, an LLM with a fine-tuned toolchain, a retrieval agent, a code writer, or even a simulation orchestrator. What matters is that they can receive a task, execute it, and return results in A2A format.
  
  
• Insight: A2A turns these agents into HTTP-addressable microservices, each with a bounded context and contract, which can be composed into larger reasoning or action pipelines.
  

‍3. A2A Client (Client Agent)

• What it is: The entity (often another agent or AI app) that orchestrates workflows by dispatching tasks to remote agents.
  
  
• Why it matters: The client becomes the conductor in a multi-agent symphony, selecting collaborators based on capability metadata, coordinating task states, and fusing intermediate outputs into coherent flows.
  
  
• Strategic insight: By abstracting over agent internals, A2A clients can easily integrate new tools, LLMs, or third-party agents without custom glue code, driving down the cost of innovation.
  

4. Task

• What it is: A typed unit of work passed from the client to the server, complete with a task_id, metadata, and a state machine (created → working → completed → failed).
  
  
• Why it matters: The task is the execution spine around which all messaging, status updates, and outputs revolve.
  
  
• Design insight: This stateful abstraction enables long-running or multi-turn tasks, which are essential for agentic behaviors like planning, iterative refinement, or recursive problem-solving.
  

5. Message

• What it is: A turn in the conversation, scoped to a task, containing structured content called Parts.
  
  
• Why it matters: Messages allow agents to reason collaboratively, asking questions, issuing updates, or providing intermediate results.
  
  
• Analogy: In traditional software, this would resemble typed events in a pub/sub system or RPCs with streaming capabilities.
  

6. Part

• What it is: A polymorphic content block inside a Message or Artifact. It can be:
  
  
  
  • TextPart – freeform or formatted natural language
    
    
  • FilePart – binary data (e.g. images, code files)
    
    
  • DataPart – structured JSON (e.g. user input forms, config blocks)
    
    
• Why it matters: Parts decouple how agents communicate from what they communicate, enabling rich and adaptive messaging that flexes between structured and unstructured data.
  

7. Artifact

• What it is: A final output of a task, typically returned by the A2A server to the client.
  
  
• Why it matters: Artifacts represent the deliverables of agent collaboration: a generated document, codebase, insight, chart, or media file.
  
  
• Insight: By using the same structure as messages, artifacts can be easily chained into new tasks, enabling recursive agent workflows without format translation.

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The Concept of Opaque Agents in A2A

A foundational design principle of the Agent2Agent (A2A) protocol is the notion of opaque agents. In this context, "opaque" means agents operate as black boxes: they expose what they can do, not how they do it.

Instead of surfacing model internals, chain definitions, or decision logic, an A2A-compliant agent only publishes its externally visible capabilities via a machine-readable Agent Card. These cards specify supported tasks, expected inputs, and authentication requirements, but not the implementation mechanics behind those tasks.

Why Opacity Matters

• Encapsulation of Logic: Developers can build agents with proprietary prompts, models, heuristics, or chain logic, all shielded from external consumers. Only the contract (i.e., what the agent promises to do) is visible.
  
  
• Interchangeability: As long as two agents support the same capabilities, they can be swapped interchangeably in an A2A system, regardless of their internal design or framework.
  
  
• Security & Isolation: Since internal data structures, LLM prompts, or execution pipelines aren’t exposed, third-party agents can be safely integrated without leaking intellectual property or compromising data integrity.
  
  
• Decoupled Architecture: Opaque agents promote clean separation of concerns. You can independently evolve or redeploy agents without affecting the rest of the multi-agent system.
  
  

This abstraction is critical for real-world deployments, especially when agents come from different vendors or span across organizational boundaries.

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The Future of the Agent2Agent Protocol

A2A is evolving quickly, with several roadmap improvements on the horizon:

• Built-in Authorization: Future Agent Cards will support formal authentication and credential schemes, reducing friction in secure agent-to-agent communication.
  
  
• Dynamic UX Negotiation: Agents will be able to adjust outputs (e.g., switch to video or forms) mid-task, enabling richer, context-aware interactions.
  
  
• Faster Messaging: Streaming and push mechanics will improve, allowing near real-time task coordination across distributed agents.
  
  

Long term, A2A is poised to become the HTTP of agent communication, a universal layer for cross-vendor, multi-agent interoperability. We’ll likely see specialized agent teams, open registries, and enterprise-wide orchestration built on top of it.

For developers, A2A (alongside MCP) marks a shift toward standardized, secure, and scalable AI systems. Now is the time to build.

The Agent2Agent protocol isn’t just a communication layer, it’s a foundational shift in how intelligent systems interact. With formal support for discovery, task management, messaging, and UX negotiation, A2A lays the groundwork for scalable, modular, and secure agent ecosystems.

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While platforms like GoCodeo focus on enabling high-performance autonomous development workflows, protocols like A2A and MCP signal where the agent landscape is headed: toward composability, interoperability, and shared semantics. The future may involve registries of reusable agents, plug-and-play coordination layers, and industry-specific agent teams, all speaking the same protocol.

For developers building with AI today, this is a call to go deeper: don’t just build agents, engineer ecosystems.