MCP vs A2A: How to Design Multi-Agent Workflows in 2026

As soon as a team ships one useful AI agent, the next request is for a second and a third, followed by the question of how they should work together. Two open protocols have emerged to answer it. They are often described as rivals, and they are not: they solve different problems at different layers of the stack.

The one-sentence difference

MCP connects an agent to its tools. A2A connects an agent to other agents. If your agent needs to query a database, create a ticket or read a file, that is MCP territory. If your agent needs to ask a specialist agent, possibly run by another team or vendor, to take on a sub-task and report back, that is what A2A is for. Our MCP primer covers the first in detail.

Do you need a multi-agent system at all?

Usually not. A single well-scoped agent, or better a workflow with model steps, handles most business processes, and every extra agent adds latency, cost and a place for errors to compound. We cover the decision in AI agents vs AI workflows vs chatbots. Split into multiple agents when at least one of these is true:

• Different permissions. The agent that reads customer data should not be the one that can issue refunds. Separate agents let you separate credentials.
• Different context. A specialist with a small, focused context and its own instructions beats a generalist stuffed with everything.
• Real parallelism. Independent subtasks, such as checking a supplier against four data sources, can run at once.
• Organizational boundaries. The other agent is owned by another team or vendor, and you need a contract rather than shared code.
• Independent review. A separate agent checking work against explicit criteria, the evaluator role, catches errors a generator misses.

A reference architecture

The pattern that holds up best in practice is a single orchestrator with a handful of narrow specialists. Here is an example for handling inbound sales inquiries end to end.

1. 01Trigger. A form submission or email arrives and starts the workflow (deterministic).
2. 02Orchestrator agent reads the inquiry, decides which specialists are needed and holds the overall goal.
3. 03Research agent (read-only) enriches the company and contact through approved data sources, using MCP tools.
4. 04Qualification agent scores fit against written criteria and returns a structured result with reasons.
5. 05Drafting agent prepares a reply and meeting proposal using approved messaging.
6. 06Reviewer agent or human checks the draft against a checklist. Anything unusual goes to a person.
7. 07Executor step (deterministic code, not an agent) writes to the CRM and sends the message once approved.

Notice what is not an agent: the trigger and the final write. Keeping the beginning and the end deterministic gives you a predictable boundary around the model-driven middle. If the specialists are all in-house and share a runtime, you may not need A2A at all, since simple function calls between them are easier. A2A earns its place when agents are separately deployed or owned. For the full sales example see AI sales automation and lead qualification.

Design rules for multi-agent workflows

• Define contracts. Each agent gets a written input and output schema. Free-text handoffs are where errors creep in.
• Cap the loop. Set a maximum number of steps, handoffs and a spend limit per run.
• Pass the minimum. Give each agent only the context it needs, both for accuracy and to limit what a compromised agent can leak.
• Keep a shared trace. One run ID across all agents so a single failure can be replayed end to end.
• Make accountability explicit. Decide which agent, and which human, owns each outcome.

The risks are different from single-agent systems

The OWASP Top 10 for Agentic Applications includes risks that only appear when agents interact: insecure inter-agent communication, cascading failures and rogue agents, on top of goal hijacking and tool misuse. A poisoned message from one agent can steer another; a bad output early in a chain can be amplified downstream. We go through them in AI agent security. Authenticate agents to each other, validate structured outputs, and treat messages from other agents as untrusted input.

Where this is heading

Platform vendors are converging on agent-as-a-worker models with identity, memory and governance built in. Microsoft's Copilot Autopilot and Agent 365 and Salesforce's Agentforce announcements at Dreamforce 2026 are examples, analyzed in our September 2026 platform roundup. Whichever platform you choose, protocol support determines how portable your agents will be. For the wider picture, start at the AI automation guide.

Sources and further reading

• Model Context Protocol
• A2A Protocol project
• Building Effective Agents, Anthropic Engineering
• OWASP GenAI Security Project