A2A vs MCP for Multi-Agent Systems (2026)

Last updated: 6 October 2026

Quick Answer: MCP connects one agent to tools and data, while A2A lets separate agents discover each other and hand off tasks. They solve different problems, so most multi-agent systems run both: MCP inside each agent, A2A between agents. Both now sit under the Linux Foundation's Agentic AI Foundation.

MCP and A2A are not rivals. They work at different layers, and confusing them is how teams over-build agent systems.

Design documents that say MCP vs A2A are really asking a sequencing question: which layer do we standardize first, and do we need the second at all. That depends on how many teams own the agents. One team shipping one agent with fifteen tools needs MCP and nothing else. Four teams whose agents call each other across deployment boundaries need both.

The landscape also moved faster than most published comparisons. MCP is on spec revision 2026-07-28, A2A reached 1.0 in March 2026, and both share a governance home. Copy that calls an older revision current, or treats ACP as a live option, describes 2025.

What is the difference between A2A and MCP?

MCP is a client-server protocol for giving one agent access to tools, data and context. A2A is a peer protocol for letting independent agents discover each other, delegate tasks and return results. MCP points inward to an agent's capabilities; A2A points outward to its collaborators. Neither does the other's job, which is why A2A vs MCP usually ends in "both".

Protocol facts as published on modelcontextprotocol.io and a2a-protocol.org on 6 October 2026.

Both documents are public: the MCP specification and the A2A specification. Two rows matter most: MCP authorization is optional, not assumed, and the Agent Card is A2A's entire discovery mechanism.

What problem does each protocol solve?

MCP solves tool and context access for a single agent. A2A solves coordination between agents that share no process, codebase or owner. Reading it as A2A protocol vs MCP suggests a bake-off that the specifications do not support.

On the MCP side a server offers three things to a client: Resources (context and data), Prompts (templated messages and workflows) and Tools (functions the model can execute). The client can offer Elicitation back, so a server can ask the user for more mid-task. That is the whole surface, carried over JSON-RPC 2.0. Revision 2026-07-28 adds opt-in extensions for long-running tasks, agent skills and inline UI.

On the A2A side the objects are a Task (the stateful unit of work), a Message (one turn, made of Parts), an Artifact (what the task produced) and the Agent Card advertising identity, capabilities, skills, endpoint and authentication requirements. The stated design goal is collaboration without agents needing access to each other's internal state, memory or tools. That opacity is the point: the billing team can change how its agent reasons without telling you.

People search for Google A2A vs Anthropic MCP as if one company's protocol has to win, but both were donated to the Linux Foundation and A2A's steering committee now includes AWS, Cisco, Google, IBM, Microsoft, Salesforce, SAP and ServiceNow.

MCP vs A2A vs function calling: which one are you actually choosing?

Function calling is a model capability: the model emits a structured call and your code runs it. MCP is the wire format and discovery layer that stops you hand-writing that integration for every tool, agent and language. A2A sits above both: it decides what happens when the callee is another autonomous agent with its own tools and lifecycle.

Can A2A and MCP work together in one system?

Yes, and that is the normal production shape. A2A carries work between agents; MCP carries work from an agent down to the systems it touches. One request can cross both layers several times without either protocol knowing about the other.

A worked example. An orchestrator agent receives "refund invoice 4471 and tell the customer". It holds no billing credentials and does not know the schema, so it delegates:

User request
   v
Orchestrator agent
   |-- A2A task "refund invoice 4471" --> Billing agent   [another team, another deployment]
   |                                         '-- MCP --> Billing server --> ledger, payments API
   '-- A2A task "notify the customer"  --> Comms agent
                                             '-- MCP --> Email server --> message queue
   ^
   '-- A2A artifacts back (credit note ID, delivery status)

Each hop is a different trust decision. The A2A hops are service-to-service: the orchestrator reads the billing agent's Agent Card, authenticates against what the card declares, and gets Artifacts back rather than raw database rows. The MCP hops sit inside that agent's own blast radius, under its credentials and tool permissions. Separating the layers is what lets you audit "which agent asked" independently of "which tool ran".

Where does ACP fit alongside A2A and MCP?

It does not, any more. IBM's Agent Communication Protocol merged into A2A on 25 August 2025, and the i-am-bee/acp repository is archived, its README pointing at A2A. Engineers still searching for MCP vs A2A vs ACP are reading material written before that merge.

A year ago the agent-interop question had three plausible answers and no safe bet. Now there is one protocol per layer, and A2A's announcement of joining the Agentic AI Foundation in August 2026 put both under one governance roof. If you inherit an ACP client, treat the move to A2A as maintenance rather than re-architecture: the task-and-artifact model survived the merge.

Which protocol should a multi-agent system start with?

Start with MCP. Almost every agent needs tools before it needs peers, and the MCP work is reusable the moment a second agent appears. Add A2A when agents are owned by different teams, written in different stacks, or run by different vendors: those are the boundaries where a shared task contract pays for itself.

Choose MCP only, and skip A2A entirely, when one team owns every agent and they all run in one deployment. In-process calls or a queue will be simpler to debug, and one agent with well-scoped tools handles more than most teams expect. How multi-agent builds get scoped in practice is a useful sanity check first. A2A is the better choice the moment an agent you do not control must be invoked by one you do, or a long-running task needs status, cancellation and push-notification semantics you would otherwise invent.

Frameworks are a separate decision. LangChain, AutoGen and MetaGPT all give you orchestration primitives, and none obliges a protocol choice: you can expose a LangChain agent behind an A2A endpoint and have it call MCP servers underneath.

What are the security risks of agent-to-agent communication?

Insecure inter-agent communication is a named entry in the OWASP Top 10 for Agentic Applications for 2026. Three of its ten risks land on this architecture: ASI07 Insecure Inter-Agent Communication, ASI02 Tool Misuse and ASI03 Identity & Privilege Abuse. The common failure is treating a trusted internal network as an authorization boundary.

Four controls do most of the work:

• Enforce MCP authentication yourself. The specification makes authorization optional. On HTTP transports it follows the OAuth 2.1 IETF draft and servers must implement RFC 9728, with Dynamic Client Registration now deprecated. Optional means nothing stops an unauthenticated server reaching production.
• Treat the Agent Card as input, not truth. Pin the cards you accept, verify the endpoint, and fail closed when one changes shape.
• Give each agent its own identity. Shared service accounts collapse ASI03 into one stolen credential. Per-agent identity plus least-privilege tool scopes is what makes an audit log useful.
• Put the tool layer behind a gateway. A central MCP gateway gives you one place for authentication, per-team quotas, tool allow-lists and traffic logs. A separate article on MCP gateway options covers those choices.

Prompt injection travels well here, because an Artifact returned over A2A is attacker-influenced text the next agent treats as instructions. Constrain tool scope and validate outputs before they reach a privileged call.

What does a production A2A plus MCP architecture look like?

Five layers: agents, A2A messaging between them, an MCP gateway, the tools and systems, and observability across all four. Each layer has one owner and one failure mode, which is what makes the thing debuggable at 2am.

The integration rule that matters most: agents reach existing systems through the interfaces those systems already expose. You wrap an API in an MCP server rather than handing an agent database credentials, which keeps the existing authorization model intact and leaves a rollback path. Established integration patterns cover the middleware and connector variants, and whether a system is better reached through an MCP server or a plain API call is a separate decision.

Roll out one workflow at a time. Model a single high-volume process with one agent and real tools, measure accuracy against the manual baseline, and only then add the second agent and the A2A edge. Teams that stand up five agents before the first workflow is correct spend a quarter debugging coordination.

What mistakes should you avoid when combining A2A and MCP?

The expensive mistakes are architectural rather than technical. Four recur.

Splitting one agent into four because A2A exists. A2A pays off across ownership boundaries, not inside them. If one team deploys all four together, you have added network calls, serialization and partial-failure handling for nothing.

Assuming the network is the authorization layer. MCP authorization is optional and A2A expects you to honour what the Agent Card declares. Neither protocol authenticates anything by default.

Writing documentation against a stale spec. Revision 2026-07-28 is current, and copy naming an earlier one misleads whoever onboards next.

Letting artifacts flow into tool calls unchecked. An Artifact from a peer agent is untrusted content. Validate it before it becomes an argument to anything privileged.

How Origins AI builds multi-agent systems with A2A and MCP

Origins AI (originshq.com) builds multi-agent systems as engineering work rather than as a platform purchase. Its AI services page lists solution architecting, automation solutions and AI agent deployment among its service blocks, and commits to integrating AI into cloud and legacy systems through APIs, middleware and custom connectors: the wrap-the-interface approach described above. The frameworks it names include LangChain, AutoGen and MetaGPT.

Where agents need authority to act, not only recommend, the agentic automation product page describes approval workflows, triage and routing, monitoring with remediation, and data operations, rolled out as process mapping, agent design with explicit autonomy boundaries, a pilot on one process, then scale. Origins AI reports 1,200 hours saved annually on average, a decision cycle moving from 45 minutes to 3 minutes, and 30 to 40 percent of routine decisions automated. Those are company figures, worth testing against your baseline.

The security controls are the ordinary ones: encryption at rest and in transit, secure authentication, continuous security monitoring and least-privilege access. Engagement models run from dedicated teams to project-based work and build-operate-transfer, and Origins AI does not publish a rate card.

Talk to an engineer

If you are deciding where the A2A edge belongs, or whether you need one at all, book a call and we will walk the workflow with you.