The debate around protocols for AI agents was often framed as a race: would MCP become the universal standard, would A2A replace it, and how many competing agent protocols could the ecosystem sustain?
By 2026, the answer is clearer. There will not be one protocol for every interaction, because the acronyms describe different system boundaries.
My conclusion from the current specifications and governance changes is simple: choose a protocol by asking what is on the other side of the connection.
- If an agent needs a tool, API, or data source, that is the MCP boundary.
- If one autonomous agent delegates work to another, that is the A2A boundary.
- If the result must drive a user interface, that is another integration layer again.
MCP deepens an agent
The Model Context Protocol standardizes how a model or agent connects to external capabilities: tools, data, APIs, and resources.
Before MCP, each integration commonly had its own conventions for describing functions, passing context, authenticating callers, and returning results. MCP provides a common interface through which a client can discover capabilities and invoke one with structured arguments.
A typical MCP interaction is short:
- An agent receives a task.
- Its MCP client obtains the available tool descriptions.
- The model selects a suitable tool.
- The client sends structured arguments to an MCP server.
- The server performs the operation and returns a result.
The MCP server does not have to be an agent. It can expose PostgreSQL, GitHub, a file system, a search service, or an internal API. The protocol makes a capability available to an agent; it does not make every capability an autonomous participant.
A2A extends the system horizontally
The Agent2Agent Protocol addresses a different problem. It allows autonomous agents to discover one another, exchange messages, and collaborate on tasks.
A remote agent may plan independently, use its own tools, preserve task state, and return intermediate artifacts. The caller does not control every internal step. It delegates a task and observes the task lifecycle.
This distinction does not necessarily match physical infrastructure. Two agents can run inside one company, while an MCP server may be hosted externally. The relevant difference is the interaction contract:
- MCP says, in effect, “perform this defined operation with these arguments.”
- A2A says, “take responsibility for this task and work toward an outcome.”
Why A2A needs a task lifecycle
A tool call is usually bounded: a request is sent, then a result or error returns. An autonomous agent may work for minutes or hours, request additional input, stream progress, and produce several artifacts.
A2A therefore models a task as a first-class entity with an identifier and state.
A client can receive updates by polling, streaming, or webhook. This is more than a remote function call: the protocol represents an ongoing collaboration whose state can survive any one request.
What changed in A2A v1.0
In March 2026, the A2A community released the first stable version described as production-ready. Version 1.0 established several architectural choices that matter in real systems.
First, the specification formalized JSON over HTTP, JSON-RPC, and gRPC bindings around a common semantic model. Teams can choose a binding that fits their infrastructure without changing the meaning of the interaction.
Second, version support became explicit at the interface level. An Agent Card can advertise interfaces for different protocol versions, allowing clients and servers to migrate from A2A 0.3 to 1.0 progressively.
Third, v1.0 added native tenant scoping. A single endpoint can route requests to multiple agents or tenants rather than requiring a separate endpoint for each deployment.
Finally, Agent Cards can carry JWS signatures over JSON canonicalized according to RFC 8785. A client can verify that a card has not been modified and that it was signed by the holder of a particular key.
What happened to ACP
The Agent Communication Protocol, developed by contributors to the BeeAI project, occupied much of the same agent-to-agent territory as A2A. In August 2025, its maintainers announced that ACP would become part of A2A under the Linux Foundation. The ACP repository was archived and now directs users toward A2A and its migration guidance.
This is meaningful consolidation. Instead of maintaining two overlapping formats, the ACP project moved its experience into the A2A ecosystem.
It is still too strong to call this the end of every “protocol war.” ACP and A2A competed at roughly the same layer. MCP and A2A continue in parallel because they describe different kinds of relationships.
Shared governance is not a shared specification
In December 2025, MCP became a founding project of the Agentic AI Foundation, a Linux Foundation directed fund. In August 2026, A2A joined AAIF as a Growth Stage project.
MCP and A2A now have a common organizational home. Neutral governance can reduce dependence on a single vendor and create a better setting for coordination across projects.
It does not merge their technical contracts. The projects still have distinct specifications, maintainers, and scopes.
| Boundary | Standard or transition | What is on the other side |
|---|---|---|
| Model or agent → tool | MCP | A function, API, data source, or resource |
| Agent → agent | A2A | An autonomous task performer |
| ACP → A2A | Migration | Consolidation within the agent-to-agent layer |
| Agent → user interface | A separate integration layer | UI state, events, and human interaction |
A practical selection rule
Architecture should begin with the contract, not with the question “Which protocol won?”
Choose MCP when:
- the operation has well-defined structured inputs and outputs;
- the calling agent controls the reasoning and sequence of steps;
- the remote capability behaves like a function, resource, or service;
- the result normally fits a bounded request-response interaction.
Choose A2A when:
- the remote participant plans its own execution;
- the work may be long-running;
- intermediate progress or artifacts matter;
- the remote agent may ask questions or require human input;
- its internal tools and reasoning should remain opaque to the caller.
Use both when one agent delegates a task to another agent and each agent reaches its own tools and data through MCP.
Protocols do not replace a trust architecture
Standardizing messages solves only part of the engineering problem. Neither MCP nor A2A automatically answers these questions:
- Should this agent receive the task?
- May it see the supplied data?
- Which actions require approval?
- Can its capability description be trusted?
- Who is accountable for the result?
- How is the impact of an error or compromise limited?
A production system still needs authentication, authorization, audit trails, least-privilege controls, tenant isolation, and independent verification of consequential results.
The easier it becomes for agents to discover peers and attach tools, the more important a separate verification boundary becomes. Discovery can identify a candidate capability; it cannot authorize execution. I explored that distinction in Agentic Resource Discovery Still Needs a Verification Boundary.
What changed, and what deserves attention next
The market did not select one universal agent protocol. Instead, a layered stack is emerging.
MCP connects agents to tools and context. A2A connects autonomous agents and supports collaboration around stateful, potentially long-running tasks. ACP’s merger into A2A reduced duplication within the inter-agent layer. The move of MCP and A2A into AAIF creates a shared governance environment without erasing their technical differences.
The next hard problem is not another wire format. It is evidence at the boundary: how a system verifies identity, capabilities, policy compliance, delegated authority, and outcomes across several independently evolving protocols.
The most useful architecture question is therefore not “Which standard should we adopt?” It is “Which boundary are we designing, and what evidence must cross it?”