In the first part we saw key conceptual differences between Google A2A and the Model Context Protocol (MCP). Let’s have a look at a few practical examples of how and when to use A2A.
When to Use A2A
Finally, let’s make it concrete with a few scenarios. When might the A2A protocol be a better choice than (or a necessary addition to) MCP?
Coordinating Complex Workflows Across Agents
Imagine a company’s IT department automating its employee onboarding process. There are multiple steps: creating accounts (IT agent’s job), scheduling training (HR agent’s job), setting up payroll (Finance agent’s job), etc. No single agent has all the knowledge or permissions to do everything. Using A2A, a “Coordinator” agent can talk to each departmental agent in turn until the workflow is complete.
This kind of multi-agent orchestration is exactly what A2A was built for.
Why not just MCP?
Without A2A, you’d have to funnel all actions through one monolithic agent using MCP tools. MCP can let an agent call an “HR database tool” or an “IT system API”, but it doesn’t inherently handle conversation or decision-making between distinct intelligent agents.
A2A provides the structure for agents to negotiate responsibilities and share updates in a long-running process (which MCP alone would leave the developer to implement ad-hoc).
Cross-Organization or Decentralized Agent Communication
Consider an automated supply chain scenario. Your inventory management is low on a product and needs to order more. The vendors has its own AI agent that processes orders. Using A2A, your agent can directly message the vendor’s agent and the two agents can have a back-and-forth conversation to confirm details, timelines, maybe negotiate price, and finalize the order. This is a peer-to-peer agent interaction across organizational boundaries.
Why A2A over MCP?
MCP works great inside one org, but MCP doesn’t define how to talk to another company’s agent that isn’t just a passive tool. In our example, the supply chain agent is an active entity with its own logic – it might respond, “We only have 80 units now, will ship those and 20 later?”, requiring further dialogue. A2A is suited for this kind of agent-to-agent negotiation. It provides the trust mechanism (auth between the companies’ agents) and the conversation thread (tasks/messages) to enable a robust exchange. Essentially, whenever you have two AI systems each acting on behalf of different stakeholders that need to interact, A2A is the appropriate protocol. It’s no surprise that A2A’s launch partners included many enterprise software companies – the goal is to let, say, a Salesforce AI agent talk to a ServiceNow AI agent in a standardized way, much like how enterprise software systems have APIs to talk to each other.
Modular AI Services (Microservices for AI)
A2A is also advantageous for designing modular AI applications. For instance, think of an AI travel assistant. You could build it as one giant agent with multiple tools: a flight search tool, a weather API tool, a hotel booking tool (via MCP). That’s feasible, but consider the modular alternative: one team builds a dedicated Weather Agent that knows how to get forecasts (and maybe has specialized logic for interpreting weather data), another team builds a Flight Search Agent, another a Hotel Booking Agent, and so on. Now your travel planner agent can simply use A2A to consult these specialist agents when needed. It might ask the Weather Agent “What’s the weather in Paris next week?” and get a nicely formatted answer, then ask the Flight Agent “Find flights from NYC to Paris on those dates,” etc. Each agent is maintained independently (perhaps even by different service providers), and as long as they all speak A2A, they can interoperate.
Why is this better?
It brings separation of concerns and scalability. Each agent can be updated or improved on its own, and new agents can be added to the ecosystem without changing the others – much like microservices in software architecture. MCP alone doesn’t provide that inter-service conversation; it would treat each external API as a dumb tool. In our example, the Weather Agent might itself be using MCP internally to fetch data from a weather API (i.e. it’s an AI agent that calls a weather tool). But from the Travel Planner’s perspective, it just sees “Weather Agent (skill: provide forecast)” and uses A2A to get a higher-level result (“It will be sunny and 75°F in Paris on Monday”). This can simplify the planner’s logic (it doesn’t have to know how to call the API or parse JSON – the Weather Agent handles that). Essentially, A2A allows an architecture of AI microservices, where MCP would imply one AI service with many plugin tentacles. When you expect to reuse specialized AI capabilities across different projects, A2A provides a more decoupled integration.
In each of the above scenarios, A2A shines because there is an element of interactive, autonomous collaboration needed – something beyond a single AI calling a tool and getting an answer. MCP, on the other hand, is superb for extending an AI with well-defined tool use in a single-agent context. In practice, robust AI applications will likely use both: MCP to give individual agents more powers, and A2A to enable multi-agent coordination. If you just need to query a database or call a single API, MCP is the straightforward choice. But if you need multiple AIs or services working in concert (with potentially complex dialogues between them), that’s when A2A becomes indispensable.
Conclusion
Google’s A2A protocol and Anthropic’s MCP address different aspects of making AI systems more powerful and useful, especially for those of us building applications with LLMs and agents.
When starting out, you don’t necessarily have to choose one over the other as they serve complementary needs. If your project is an AI assistant that needs internet access or database queries, you’d look at MCP. If your project involves building a suite of AI agents each handling a part of a workflow, A2A will be key to getting them to talk to each other. As these protocols mature, we may see an emerging “agent internet” where specialized AI services discover and call upon each other on the fly. Understanding A2A and MCP now will put you ahead of the curve in designing such systems.
Both protocols are open-source initiatives backed by major AI players, and they’re rapidly evolving. It’s an exciting time – akin to the early days of web standards – where adopting the right protocol can set the stage for interoperability and scalability in your AI projects. We hope this overview gave you a clear starting point. With A2A enabling agent teamwork and MCP enabling tool usage, the AI agents you build can become both well-informed and highly collaborative – a combination that promises far more capable AI applications in the near future. Happy building!