A2A and MCP are two complementary open protocols for AI agents. MCP (Model Context Protocol, originally from Anthropic, November 2024) connects an agent to tools, data and context - this is the agent-to-tool layer. A2A (Agent2Agent, originally from Google, April 2025) lets independent agents collaborate with one another - this is the agent-to-agent layer. They do not compete. They solve different problems and are used together in practice.

Once you have internalised that these are two layers of the same stack - not two camps in a standards war - you quickly make better architecture decisions.

• MCP gives an agent tools. It standardises how an agent queries a database, reads from a CRM, opens a file or triggers a web search.
• A2A lets agents work together. It standardises how an agent delegates a task to another, independent agent - even across system and vendor boundaries.
• The two complement each other. The official rule of thumb from Google, Salesforce and Microsoft is: "MCP is for capabilities (agent-to-tool). A2A is for collaboration (agent-to-agent)." Following the merger of its ACP project, IBM confirms the same line.

The mental model: hands versus a shared language

A memorable image: MCP gives an agent hands - the ability to reach, read and act in the outside world. A2A gives several agents a shared language - the ability to issue instructions to one another and return results, without either having to expose its prompts, models or memory to the other.

The decisive conceptual move in A2A is that the internal logic of the remote agent remains opaque. A2A defines how agents talk to one another, not how they think. This is precisely what allows a Salesforce agent to call an SAP agent without either of them having to reveal its internal prompts. MCP, by contrast, exposes a tool with a clearly described schema - here transparency about the capability is exactly the point.

Technical comparison: dimension, MCP and A2A

Both protocols build on JSON-RPC 2.0 and sit under the umbrella of the Linux Foundation - underlining that they are intended as one coherent stack.

MCP saw an outright adoption explosion in 2025: OpenAI adopted it in March 2025, Google DeepMind in April 2025, alongside Microsoft Copilot Studio, Cursor, Zed, Replit and many more. A2A is now supported by more than 100 companies; the founding members of the Linux Foundation project are AWS, Cisco, Google, Microsoft, Salesforce, SAP and ServiceNow.

Why "complementary" is meant literally

There is a technical grey area that often causes confusion in discussions: you can expose an agent as an MCP server and thereby make it addressable to other agents in tool-shaped form. This works - but it was not the design purpose of MCP, and Anthropic, Google and Microsoft consistently recommend A2A for genuine peer collaboration.

The reason lies in the semantics. MCP knows a tool call: request in, response out. A2A knows a complete task life cycle with intermediate states, built for long-running, possibly clarification-seeking collaboration - including the input-required state when the remote agent needs clarification. Anyone rebuilding agent-to-agent over MCP loses precisely this mechanism.

Historically there was briefly a third protocol, IBM's ACP (Agent Communication Protocol, March 2025). It was absorbed into A2A under LF AI & Data on 29 August 2025 - the ACP design principles (REST, async-default) live on in A2A. The remaining picture for 2026 is therefore clear: MCP + A2A is the convergent stack. AGNTCY (Cisco and others) is an identity, discovery and observability layer on top of A2A and MCP, not a competitor. NANDA (MIT Media Lab) is research and does not belong in production decisions in 2026.

Combined example: claims handling with both protocols

A concrete, simplified example makes the interplay tangible. Imagine an insurance workflow - in the spirit of the documented Allianz project "Nemo", which uses seven specialised agents (Planner, Cyber, Coverage, Weather, Fraud, Payout, Audit) and runs a claim through in under five minutes (with a human in the loop for the final payout decision).

Pseudocode from an architecture perspective:

```

A2A layer: orchestrator delegates to specialised peer agents

orchestrator.send_task(coverage_agent, task="Check coverage for case #4711")
orchestrator.send_task(fraud_agent, task="Check fraud indicators #4711")
orchestrator.send_task(weather_agent, task="Storm data for the date of loss")

Each peer agent uses MCP INTERNALLY to access its tools:

coverage_agent -> MCP server "policy database" (SQL lookup)

fraud_agent -> MCP server "fraud scoring API"

weather_agent -> MCP server "weather service API"

A2A task life cycle: submitted -> working -> completed

Results return as A2A artefacts, orchestrator synthesises

```

The distribution of tasks between the agents runs over A2A - the orchestrator does not know, and does not need to know, which model or which prompt sits inside the fraud agent. But as soon as an individual agent queries the policy database or addresses a weather API, this runs over MCP. A2A coordinates the minds, MCP operates the hands. It is precisely this clean separation that is the reason the two protocols are regarded as complementary.

To put the cost and benefit dimension in context: Anthropic's documented multi-agent research system (lead model plus parallel sub-agents) achieved, internally, +90.2% on research breadth compared with a single agent - at roughly 15 times the token consumption. Multi-agent architecture (and thus A2A) therefore pays off above all for parallelisable, broadly spread tasks, not as an end in itself.

DACH reality: complementarity has a compliance price

For DACH B2B decision-makers, one consequence matters that is rarely named honestly in US-centric reference architectures: when agents run over A2A at different vendors and incidentally fire off MCP tool calls, the data flow crosses several processing and possibly data-residency boundaries with every task.

Every A2A hop and every MCP server is potentially its own processor in the sense of the GDPR (Art. 28). Complementarity at the protocol level therefore means, in practice, a longer DPA chain and - for cross-border hops - a separate transfer question per hop (Art. 44-49). Anyone operating a hybrid Salesforce-plus-SAP-plus-Microsoft landscape should log these hops with an end-to-end trace ID from the outset and document the DPA chain per agent.

For agencies and B2B decision-makers

The practical recommendation is unambiguous: MCP for tools, A2A for agents - anything else is a deliberate deviation from the convergent stack in 2026 and needs justification. For DACH SMEs this means, concretely, building MCP competence first (you will write or consume MCP servers in any case) and introducing A2A as soon as agents have to collaborate across system and vendor boundaries.

For agencies and AI-adjacent product vendors, the greatest strategic lever is to publish an AgentCard for every productised agent. This lets a customer base call your agent directly from Agentforce, Joule or Copilot Studio via A2A - without costly point integrations. Anyone looking to design a multi-agent strategy for their company or their clients should start exactly here: clear protocol separation, documented DPA chains and A2A discoverability from day one. Get in touch if you want to set up this architecture robustly for your DACH context.