Protocol map
AI Agents Are Getting a Protocol Stack: How MCP and A2A Fit Together
MCP connects agents to tools and context. A2A gives independent agents a way to discover one another, exchange work, and manage long-running tasks. The distinction is becoming an architectural boundary.
The interface problem has split in two
The first generation of agent products often bundled everything behind one interface: the model, the instructions, the connectors, and the logic for carrying work from one system to another. That was enough to demonstrate capability. It is less useful when an organization has many agents, many data sources, and several vendors that all need to participate in the same workflow. Every custom connection becomes another dependency to maintain, secure, and explain.
Two open protocols are emerging around different sides of this problem. Anthropic introduced the Model Context Protocol, or MCP, to standardize how an AI application connects to tools and context. Google introduced Agent2Agent, or A2A, to standardize how independent agents discover one another and collaborate. They are sometimes presented as competing standards because both sit near the agent layer. Their own specifications describe a more useful relationship: MCP addresses an agent-to-resource boundary, while A2A addresses an agent-to-agent boundary.
That division is becoming a protocol stack. An agent can use MCP to query a database, read a document, or invoke a business tool, then use A2A to delegate a separate piece of work to another agent operating under a different owner or technology stack. The protocols do not make the workflow intelligent by themselves. They define repeatable interfaces around the intelligence.
MCP standardizes the agent-to-tool boundary
MCP uses a host-client-server architecture. A host application manages one or more MCP clients, and each client connects to a server that exposes capabilities. The core server features are tools, resources, and prompts: actions the model may invoke, contextual data the application may retrieve, and reusable interaction templates. The protocol uses JSON-RPC messages and capability negotiation so each side can state what it supports.
The current MCP specification, dated July 28, 2026, also makes the transport model more web-native. Requests can be self-contained and routed without relying on a long-lived session, while discovery tells a client which methods and extensions are available. Optional extensions now cover work that outlives a single request, including Tasks, and packaged capabilities through Skills. These additions expand MCP beyond a simple connector format without changing its central role: a controlled interface between an AI application and an external capability.
OpenAI's Agents SDK shows how MCP enters an application architecture in practice. Developers can connect agents to hosted MCP tools, streamable HTTP servers, legacy SSE servers, or local standard-I/O servers. The SDK also exposes filtering, caching, tracing, and approval controls. The point is not merely that a model can call more tools. A product team can create one governed tool boundary and make it available across agents without rebuilding every integration for every model.
A2A standardizes the agent-to-agent boundary
An A2A participant is not treated as a transparent tool. It is an agent with its own instructions, memory, security policy, and implementation details. An Agent Card advertises what it can do and how to reach it. A client agent can then send a message or create a task, receive status updates, and collect artifacts produced by the work. The remote agent is free to decide how the task is completed.
That opacity matters. A procurement agent should not need to know whether a supplier's fulfillment agent uses a particular model, orchestration framework, or database. It needs a dependable contract for requesting work and interpreting the result. A2A 1.0 defines that contract across JSON-RPC, gRPC, and HTTP/REST bindings, with support for synchronous responses, streaming updates, and push notifications for long-running work.
Google launched A2A in April 2025 with more than 50 technology partners and framed it as complementary to MCP. The design emphasized enterprise authentication, capability discovery, multiple content types, and tasks that may take minutes, hours, or longer. Those concerns are different from exposing a function to a model. They resemble distributed workflow coordination between software systems that happen to be agentic.
The stack is compositional, not competitive
A useful mental model is to place MCP inside an agent and A2A between agents. Consider a customer-support agent that receives a request to replace a damaged product. It might use MCP servers to retrieve the order, check policy, and create a case. If the replacement requires a decision from a logistics agent operated by a different team, it can send that task over A2A. The logistics agent may then use its own MCP connections to inspect inventory and schedule shipment.
The A2A specification makes this relationship explicit: an A2A server can use MCP internally to reach tools, resources, or data. The reverse can also be useful at the application level. An MCP tool could present a narrow interface to an A2A-enabled service, allowing a host to treat remote delegation as one governed capability. Architecture, not protocol branding, should determine which boundary is visible.
There will still be overlap at the edges. MCP now supports longer-running tasks, while an A2A agent may expose a capability that looks simple enough to be a tool. The deciding question is ownership and autonomy. If the caller should control a well-defined operation, MCP is the clearer abstraction. If the caller is delegating an outcome to a separately governed actor that manages its own process, A2A is the stronger fit.
Discovery is becoming part of the product
Protocols become strategically important when discovery and substitution become possible. MCP clients can learn which tools, resources, prompts, and extensions a server provides. A2A clients can read an Agent Card to understand an agent's skills, endpoints, authentication requirements, and supported interaction modes. In both cases, the interface describes capability before a workflow depends on it.
This does not create an automatic marketplace of trustworthy agents. Descriptions can be incomplete, capabilities can change, and an advertised skill says little about quality under real conditions. Product teams will need registries, verification, version policies, evaluation data, and explicit allowlists. Discovery makes components legible; governance determines whether they are safe to use.
The organizational consequence may be as important as the technical one. Teams can publish stable boundaries around internal capabilities instead of handing every new agent direct access to the underlying system. Vendors can support a protocol without dictating the complete orchestration stack. Buyers gain some freedom to replace a model, connector, or specialist agent while preserving the surrounding workflow.
Security is where the diagram becomes a system
A clean architecture diagram can hide several principals: the person using the host, the host application, the model provider, the MCP server, the remote A2A agent, and every system reached downstream. Each may have a different identity and permission set. Passing one broad credential through the chain defeats the value of the protocol boundaries and makes it difficult to answer who authorized an action.
The MCP specification treats user consent, data privacy, tool safety, and authorization as core concerns. OpenAI's documentation similarly advises developers to connect only to trusted servers, apply least privilege, handle authorization headers carefully, and require approval for sensitive operations. A2A defines authentication and authorization at the protocol boundary, but an Agent Card is not proof that a remote agent deserves access. Identity, policy, and trust evaluation remain deployment responsibilities.
The practical pattern is delegated authority with narrow scopes. An agent should receive only the credentials and data required for the current task. High-impact actions should cross an approval gate. Inputs and outputs should be validated at every trust boundary, and audit records should connect the original request to every tool invocation, delegation, and human decision. Open protocols make these controls easier to standardize; they do not supply the policy.
Builders need one operational trace
A workflow that crosses MCP and A2A can fail in ways that look similar to the user but have different causes. The model may select the wrong tool. An MCP server may return stale data. A remote agent may accept a task but misinterpret the desired outcome. A callback may arrive after the initiating process has expired. Logs confined to one component will not explain the complete result.
Production systems therefore need a shared trace that follows the work across protocol boundaries. Useful records include the user intent, policy decision, agent and server identity, capability version, request and task identifiers, tool inputs, artifacts, latency, cost, approvals, and final outcome. Evaluation should distinguish transport success from task success. A technically valid response is not evidence that the business objective was met.
For a systems integrator such as Awayvo, the practical opportunity is to use MCP for controlled access to a customer's operating systems and reserve A2A for collaboration across a genuine agent or organizational boundary. The protocols can reduce bespoke plumbing, but the valuable implementation work remains: deciding who owns each action, which data can cross the boundary, when a person must approve, and how the complete workflow is measured.
The protocol layer is becoming strategic
MCP's institutional path is a signal of how quickly this layer is maturing. In December 2025, Anthropic donated MCP to the Linux Foundation's new Agentic AI Foundation, co-founded with Block and OpenAI, with support announced by organizations including Google, Microsoft, Amazon Web Services, Cloudflare, and Bloomberg. A protocol that began as one company's connector standard is now being developed as shared infrastructure.
A2A has followed a similar open-governance direction while moving to a 1.0 specification. The important contest is no longer simply which company can assemble the largest collection of proprietary integrations. It is which products can use common interfaces while delivering better reliability, security, discovery, evaluation, and user control above them.
The emerging stack is still young. Implementations will diverge, extensions will compete, and many workflows do not need multiple agents at all. But the architectural distinction is durable: agents need a standard way to reach capabilities, and independently governed agents need a standard way to coordinate work. MCP and A2A give those two relationships names. The companies that make the boundaries trustworthy may create more lasting value than those that merely connect the most components.
Primary sources
Model Context Protocol specification · July 28, 2026Agent2Agent Protocol specification · version 1.0Google Developers Blog · Announcing the Agent2Agent ProtocolOpenAI Agents SDK · Model Context ProtocolAnthropic · Donating MCP and establishing the Agentic AI Foundation