A2A Protocol Explained: The Future of AI-to-AI Conversations and Collaboration

Introduction

Artificial Intelligence is moving beyond the era of single, siloed assistants into a new phase—ecosystems of specialised agents. From planning an international trip to orchestrating a global supply chain or managing a smart grid, the future will increasingly involve multiple AI agents, often built by different organisations, collaborating in real time.

The challenge?

• Each agent “speaks” a different technical language.
  
• Traditional APIs handle single-step, predictable calls, but falter in long, evolving, negotiation-heavy workflows.
  
  

Enter the Agent2Agent (A2A) Protocol—launched by Google in April 2025 and now governed by the Linux Foundation. It provides a common, open standard for AI-to-AI communication, making multi-agent cooperation stateful, secure, and scalable.

1. Managing Multi-Turn AI Conversations: A2A’s Task-Based Workflow

Most integration methods treat requests like one-off emails—send, wait, done. But real-world collaboration is more like a project plan with milestones, check-ins, and shifting requirements.

A2A solves this by introducing Tasks—stateful workflows with clearly defined statuses:

• submitted
  
• working
  
• input-required
  
• completed
  
  

How It Works

1. Discovery – Agents identify each other using Agent Cards—machine-readable “business cards” listing skills, endpoints, and security credentials.
   
   
2. Authentication – Secure handshakes via OAuth 2.0, API keys, or OpenID Connect.
   
   
3. Communication – JSON-RPC 2.0 over HTTPS, featuring:
   
   
   • Multi-modal content (text, structured data, files).
     
   • Real-time updates via Server-Sent Events (SSE).
     
   • Push notifications for long-running operations.
     
     

Example – International Trip Planning
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A single AI assistant could coordinate the following workflow:

Each task updates in real time as agents complete their part, without the primary assistant micromanaging every step.

2. A2A vs MCP: Complementary, Not Competitive

While A2A governs agent-to-agent coordination, the Model Context Protocol (MCP) connects AI models to external tools, APIs, and data sources. Think of MCP as how an agent uses its tools and A2A as how multiple agents work together.

Key Differences

3. Beyond Traditional APIs: Why A2A is a Game-Changer

The Limits of APIs

‍APIs are excellent for predictable, one-shot queries—like “give me today’s weather.” But they struggle with fluid, multi-step, collaborative processes that require mid-course corrections, negotiation, and asynchronous progress tracking.

A2A Advantages:

• Agent Discovery – No need for hardcoded integrations; agents advertise capabilities via Agent Cards.
  
• Continuous Collaboration – Real-time updates and push-based coordination.
  
• Privacy by Design – Agents don’t need to expose internal algorithms or data.
• ‍Universal Compatibility – Works across tech stacks using plain HTTP and JSON-RPC.

4. Execution Layer: A2A Meets Domain-Driven Context Engineering (DDCE)

Defining a protocol is one thing. Executing it well is another. A2A handles how agents talk, but not what context they carry into the conversation. That’s where Domain-Driven Context Engineering (DDCE) comes in—a methodology for ensuring each agent operates with domain-specific clarity.

DDCE Principles in A2A Execution

• Bounded Contexts – Each agent owns a clear scope (e.g., inventory tracking, procurement, logistics).
  
• Shared Language – Agents agree on consistent domain terms, reducing misinterpretation.
  
• Context Boundaries – Prevents scope creep and keeps workflows manageable.
  
  

Why It Matters

‍Without domain discipline, multi-turn conversations can drift, creating inconsistent outcomes. With DDCE, agents act like microservices—highly specialised, predictable, and composable.

Example – Supply Chain Execution

1. Inventory Agent flags a shortage.
   
2. Procurement Agent negotiates replenishment with suppliers.
   
3. Logistics Agent schedules delivery.
   
4. All coordination happens over A2A, but domain rules ensure accuracy and efficiency.
   
   

Execution Stack Model

[Domain Context Layer]

   ↳ Defines entities, workflows, domain language

[A2A Protocol Layer]

   ↳ Handles discovery, authentication, message transport

[Agent Logic Layer]

   ↳ Implements skills within bounded contexts

[Tool/Resource Layer]

   ↳ External APIs, MCP-connected tools

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By combining A2A’s interoperability with DDCE’s structured domain clarity, organisations can scale AI collaboration without losing precision.

However,

A2A is not here to replace APIs or MCP.

It’s the collaboration fabric that enables complex, stateful, multi-agent workflows to thrive across different vendors, platforms, and problem domains.

Pair it with Domain-Driven Context Engineering, and you get more than a protocol—you get a full-stack approach to building AI ecosystems that are both technically interoperable and semantically aligned.

🔗 Next Steps:

• Explore the [A2A Protocol Specification]
• Start building your first A2A-compliant agent with domain-bound context models
• Check out initializ.ai/blog to learn more and keep up with all AI updates :)

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