MCP vs A2A: Key Differences, Architecture, and When to Use Each

In April 2026, the Linux Foundation reported that the Agent2Agent protocol had support from more than 150 organizations and over 22,000 GitHub stars. That momentum makes the question urgent, but not complicated: MCP and A2A standardize different boundaries. MCP primarily connects an AI application to tools and context. A2A connects independently operated agents so they can discover one another, delegate work, and track tasks.

They are complements, not interchangeable rivals. A production system may use MCP, A2A, both, or neither. The right choice depends on who owns the remote runtime, whether work is a bounded call or a delegated goal, and what must be authorized, observed, approved, and recovered.

Protocol capabilities and specifications evolve. This comparison was reviewed against the current MCP and A2A documentation in September 2026.

The short answer: use MCP for application-to-capability access. Use A2A for agent-to-agent delegation across an independent runtime. Use both when collaborating agents also need standardized access to tools and data.

MCP vs A2A at a glance

The familiar summary, "MCP connects agents to tools while A2A connects agents to agents," is useful but incomplete. A multi-agent system can use MCP without A2A when one orchestrator controls every agent. An A2A agent can use MCP internally. And a normal API may be better than either protocol for a deterministic service call.

For protocol-level introductions, read our Model Context Protocol guide and A2A protocol guide. Those articles go deeper than this comparison should. This page owns the architectural decision between them.

What is MCP?

Model Context Protocol is an open standard originally introduced by Anthropic. Its core architecture has three roles: a host application, one or more MCP clients, and MCP servers. A server exposes structured capabilities such as tools, resources, and prompts. The host controls the model interaction and decides which capabilities are available.

User
  ↓
Agent application / MCP host
  ↓ MCP client
MCP server
  ↓
API, database, filesystem, or business system

The important boundary is between an application and a capability provider. The model does not usually "speak MCP" by itself. The host implements the client and mediates access. That makes MCP useful for replacing one-off adapters with reusable integrations across compatible applications.

MCP does not automatically create an autonomous agent. It also does not provide complete workflow orchestration, global policy enforcement, end-to-end observability, or human approval. Authorization support is not the same as a safe authorization design. Teams still need least privilege, credential isolation, consent, and action-level policy.

Specification - Model Context ProtocolModel Context Protocol

What is A2A?

Agent2Agent is an open standard introduced by Google in 2025 and later contributed to the Linux Foundation. It connects distinct agents that may use different vendors, frameworks, models, or infrastructure. Under the official A2A specification, an agent advertises identity and capabilities through an Agent Card, accepts messages, manages tasks, and returns results as artifacts.

User or orchestrating system
  ↓
Client agent
  ↔ A2A
Remote agent
  ↓
Its own models, tools, policies, and workflow

The remote agent remains an independently implemented actor. The caller delegates a goal without needing direct access to the remote agent's memory, tools, or internal plan. That is different from invoking a bounded tool contract.

A tool should usually perform a defined operation. An agent may interpret a goal, plan how to pursue it, request clarification, maintain long-running state, and return evolving results. Wrapping every function as an agent adds needless autonomy. Hiding a sophisticated remote agent behind a simple tool call can erase task state, cancellation, and collaboration semantics.

August 25, 2026

A2A Protocol: How Autonomous Agents Communicate

The A2A protocol is the open standard for agent-to-agent communication. Here is how it works, how it differs from MCP, and what it means for building and governing multi-agent systems in production.

MCP vs A2A architecture: where the boundary sits

The protocol choice becomes clearer when you draw the ownership boundary.

MCP keeps orchestration with the host

With MCP, the host controls the conversation, model, capability selection, and usually the workflow. The MCP server adapts an underlying system into standardized primitives. If an agent queries a warehouse, creates a ticket, or retrieves a contract, it is calling a capability under the host's control.

A2A delegates into another runtime

With A2A, the calling agent sends work to another agent that owns its execution choices. The remote agent may run in another cloud, company, security domain, or framework. It can accept, reject, clarify, update, or complete a task without revealing its internal implementation.

The combined architecture

Consider a procurement workflow:

1. A procurement agent receives a purchase request.
2. It discovers a vendor-research agent through A2A.
3. The remote agent accepts a task and returns structured findings.
4. Internally, that agent uses MCP servers to query a catalog and CRM.
5. A policy layer checks the proposed supplier and budget.
6. A person approves the consequential purchase.
7. The system records the decision, execution, and result.

The nesting matters: A2A runs between agents; MCP can run inside either agent. Neither protocol replaces the operational controls around the full workflow.

{
  "remote_party": "vendor research service",
  "bounded_function_or_agent": "independent agent",
  "runtime_owner": "external team",
  "requires_task_state": true,
  "internal_capabilities": ["catalog search", "CRM lookup"],
  "approval_before": ["purchase order creation"],
  "recommended_stack": ["A2A between agents", "MCP for internal tools", "operations layer for policy and approval"]
}

Key differences between MCP and A2A

Communication target

MCP targets capabilities and context exposed by servers. A2A targets agents that accept and manage delegated work. This distinction is more useful than counting how many agents appear in a diagram.

Discovery

MCP clients discover server capabilities. A2A clients discover an agent's advertised identity and skills. In both cases, discovery creates a trust question. Metadata must be authenticated and evaluated, not merely found.

Interaction and task model

MCP often looks like structured capability invocation and context exchange. A2A is designed for goal-oriented interactions that may require clarification, intermediate updates, and long-running tasks. Both standards are evolving, so treat "often" and "primarily" as important qualifiers.

State and lifecycle

A demo ends when a result appears. Production work needs cancellation, retries, timeouts, idempotency, partial results, and resumability. A2A makes task lifecycle a central abstraction. MCP can participate in task-oriented workflows, but its foundational boundary remains capability access.

Trust and security

Authentication proves an identity. Authorization decides what that identity may do. A delegated agent can cross vendor and organizational boundaries, while an MCP server may expose sensitive tools or data. The MCP authorization specification defines protocol mechanisms, but both designs still must address credential propagation, prompt injection, data leakage, confused-deputy risks, and untrusted artifacts.

Operational complexity

Every additional runtime adds failure surfaces, version compatibility, distributed tracing, cost, and latency. A broken Zap stops at a visible step. A broken autonomous agent may continue confidently. Protocol compliance does not make the outcome safe.

When should you use MCP?

Choose MCP when one application or agent needs standardized access to tools, APIs, files, or data, and you control the orchestration.

• The capability has a bounded contract.
• The integration should be reusable across compatible hosts.
• The host should retain capability selection and workflow control.
• You are replacing bespoke model-to-system adapters.
• The remote party does not need to independently plan or manage a long-running task.

Typical examples include querying a warehouse, retrieving documents, creating a ticket, calling an internal business API, or running a controlled deployment operation. Avoid exposing broad administrative actions without granular permissions and approval controls.

Building an agent that needs MCP tools? Connect services and keep every tool call observable in Rerun.

When should you use A2A?

Choose A2A when independently deployed agents need to collaborate and the remote party should retain control over how work is completed.

• Agents come from different teams, vendors, or organizations.
• Work may be long-running or require clarification.
• Discoverable skills and explicit task state matter.
• The remote runtime should remain opaque and independently controlled.
• The job is not merely a simple deterministic API call.

Examples include a support agent delegating an investigation to a product specialist, a procurement agent working with supplier agents, or a customer-side agent interacting with a vendor-operated service agent.

Do not adopt A2A merely because several internal functions have been labeled "agents." If one orchestrator owns them all, an internal framework plus MCP may be simpler.

When should you use MCP and A2A together?

Use A2A for interoperability and delegation between independent agents. Use MCP for the tools and context each participating agent needs. Then add governance and observability across both layers.

This layered pattern preserves modularity without pretending communication standards solve operations.

The operational layer MCP and A2A do not replace

Protocols move structured information. They do not answer who is accountable when an agent chooses the wrong supplier, leaks a document, repeats a payment, or stalls halfway through a task.

End-to-end observability

Teams need correlation IDs across agent messages and MCP calls, a task timeline, inputs, decisions, tool calls, outputs, failures, cost, and latency. A chat transcript is not an audit trail. A flowchart is not a live execution record.

Governance and policy enforcement

Production policy covers which agents may contact which services, which tools they can call, what data they can access, and what budget or rate limits apply. Provenance and approval thresholds must survive across protocol boundaries.

Human-in-the-loop operations

A human approval must block the action, not merely generate a notification. Operators also need to answer clarification requests, intervene after partial execution, and resume the workflow without losing state.

Rerun fits here as a framework-agnostic operations layer. It lets teams watch agent work live, inspect logs and tool calls, pause for approval, and handle exceptions. It does not replace MCP, A2A, or the framework inside an agent.

Already using MCP or A2A? Add live execution visibility, approval gates, and exception handling without replacing your protocol stack.

July 31, 2026

Human-in-the-Loop AI Agents: The Complete Guide to Building Agents You Can Actually Deploy

Human-in-the-loop AI agents pause on high-stakes actions to get human approval. Here are the approval gates, confidence thresholds, and escalation patterns that make agents production-ready.

MCP or A2A? A practical decision framework

Ask four questions before adding a protocol:

1. Is the remote capability a bounded function or an independently operating agent?
2. Who owns the remote runtime and decision process?
3. Does the interaction require task state, clarification, or asynchronous updates?
4. What must be observed, authorized, approved, or recovered?

If those answers are vague, the protocol diagram is premature.

Production checklist

• Authenticate every agent and capability provider.
• Grant least-privileged tool and data access.
• Validate Agent Cards and capability metadata.
• Propagate correlation IDs across A2A and MCP calls.
• Define timeouts, retries, idempotency, and cancellation.
• Treat remote artifacts and tool output as untrusted input.
• Pause consequential actions for explicit approval.
• Preserve task state for recovery and manual intervention.
• Record decisions, tool calls, approvals, and outcomes.
• Test partial failure across protocol boundaries.

The bottom line

MCP standardizes access to capabilities and context. A2A standardizes collaboration between independently operating agents. Mature systems may combine them, but neither is a prerequisite for every agent system.

The more consequential question is what happens after the protocols connect. Reliable agent operations require visibility, policy, approvals, and recovery. Zapier-style flowcharts coordinate fixed steps. Chatbots return text. Autonomous work needs an operating layer that shows what happened and lets a person intervene before a risky action becomes an irreversible result.

The MCP vs A2A decision is therefore less about choosing a winning protocol and more about identifying the boundary being standardized.