A2A (Agent-to-Agent) standardizes communication between agents across organizational boundaries, letting one agent delegate tasks to another agent owned by a different organization. MCP connects an LLM app to tools and data sources. They are complementary: a single agent can use MCP to call tools and A2A to delegate to peer agents. They are not competing.
Picture an AI agent as a worker. It needs two very different things: tools to do its own work, and colleagues to delegate work it cannot do alone.
**MCP** (Model Context Protocol) covers the first need. It is the standard way an agent reaches down to tools, files, and data: a weather API, a database, a filesystem. This is the vertical direction, agent down to its capabilities.
**A2A** (Agent2Agent) covers the second need. It is the standard way one agent talks to another agent, often owned by a different company. A travel agent might delegate hotel search to a hotel-specialist agent. This is the horizontal direction, agent across to peer agents.
They are complementary, not competing. A single agent uses MCP to call its own tools and A2A to hand off whole tasks to outside agents. Most candidates wrongly assume one replaces the other. In practice a serious 2026 system speaks both at once.
They solve problems on two different axes, so they are complementary rather than competing.
**MCP** is vertical: agent to tool. It standardizes how one LLM application discovers and invokes tools, resources, and prompts on servers it controls. The other side is a passive capability provider, not an intelligent actor.
A2A (Agent2Agent), open-sourced by Google in April 2025, is horizontal: agent to agent. It standardizes how two autonomous agents, often owned by different organizations, discover each other and delegate tasks. The key difference is that the peer is opaque. You send a goal, not a function call. The remote agent plans, may ask clarifying questions, streams progress, and returns a result. You never see its internal tools.
The distinction A2A adds is delegation across a trust boundary. MCP assumes you own and orchestrate the thing you call. A2A assumes the other side is a black-box peer with its own reasoning. A2A defines Agent Cards for discovery, tasks with lifecycle states, and streaming updates.
In 2026 the consensus is layered. Use MCP to give each agent its tools. Use A2A to let agents collaborate. Anthropic and Google both back this split, and A2A moved to the Linux Foundation in mid-2025.
Quick reference
TL;DR
MCP connects one agent down to tools and data (vertical); A2A connects agents to peer agents across org boundaries (horizontal). Different layers, complementary, not competitors.
Think of a general contractor renovating a house. MCP is the contractor's toolbox: the drills, saws, and tape they personally reach for. A2A is the contractor phoning a licensed electrician from another company to wire the kitchen. The toolbox helps the contractor do their own work. The phone call lets them delegate a whole sub-job to a specialist they do not employ and cannot micromanage. Both happen on the same renovation. One is about reaching tools, the other about coordinating with independent professionals. An AI agent works the same way: it uses MCP to grab data and run functions itself, and uses A2A to hand a task to another company's agent it cannot see inside.
Everything you need to truly understand this topic: intuition, mechanics, step by step explanation, code, formulas, and worked example. Click to expand.
Everything you need to truly understand this topic: intuition, mechanics, step by step explanation, code, formulas, and worked example.
A single agent that only needs its own tools and data If there is no second autonomous agent to delegate to, A2A adds a collaboration layer with no peer; plain MCP covers everything.
Sub-agents you fully own and orchestrate in one process If you control every sub-agent's code and schema, a direct in-process call or framework handoff is simpler than an opaque cross-org protocol.
Tight latency budgets for a single deterministic capability A2A delegation implies planning, streaming, and a task lifecycle; for a fixed deterministic operation a direct MCP tool call is far cheaper.
Quick scan · 2–4 min
Everything important, quickly.
4 min: vertical versus horizontal axis + what each protocol connects + opaque peer versus passive tool + Agent Card discovery + how one agent runs both.
Side-by-side
Concern
MCP
A2A
Axis
Vertical, agent to tool
Horizontal, agent to agent
Callee
Passive tool or data server
Autonomous opaque peer agent
Unit of work
A single tool or resource call
A delegated task with a lifecycle
Trust boundary
Inside what you orchestrate
Across organizations, black box
Discovery
tools/list on a server
Agent Card advertising skills
Origin
Anthropic, November 2024
Google, April 2025, then Linux Foundation
Real-world examples
Real products, models, and research that use this idea.
1Google open-sourced A2A in April 2025 with 50-plus launch partners including Salesforce, SAP, and MongoDB, then donated it to the Linux Foundation in mid-2025.
2An A2A travel agent delegates hotel search to a specialist agent via an Agent Card, while each agent uses MCP servers internally for its own databases and APIs.
3Frameworks like LangGraph and CrewAI in 2026 wire MCP for tool access and A2A for cross-agent handoff in the same orchestration.
Interviewer follow-ups
What an interviewer would ask next. Try answering before peeking at the approach.
QHow does an A2A client discover what a remote agent can do before delegating?
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QWhere does MCP fit inside an agent that is itself an A2A server?
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QWhat new security risks does A2A add beyond MCP's tool poisoning surface?
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Don't say this
Red flags and common mistakes that signal junior thinking. Click to expand.
Most common mistake
Calling A2A a competitor that replaces MCP. They sit on different axes, vertical agent to tool versus horizontal agent to agent, and a real system runs both.