What is the Agent-to-Agent (A2A) protocol?
The Agent-to-Agent (A2A) protocol is an open standard that lets independent AI agents — built on different frameworks, by different teams or vendors — discover each other, exchange tasks, and collaborate without sharing their internal code, memory, or tools. It gives agents a common language to talk to other agents, the same way HTTP gave web servers a common language to talk to browsers.
A2A was introduced by Google in 2025 and subsequently handed to the Linux Foundation for open, vendor-neutral governance. That governance detail matters: it signals the protocol is meant to be neutral infrastructure, not a single company's lock-in.
In one sentence: A2A is the interoperability layer that turns a collection of isolated agents into a network that can delegate work to each other.
Why does agent interoperability matter now?
Most companies don't build one agent. Within a year of adopting agentic AI, they have several — a support agent, a research agent, a data-analysis agent, a procurement agent — often spread across teams and tech stacks. Some are built in LangGraph, some in CrewAI, some as no-code flows, some bought off the shelf.
Without a shared standard, connecting those agents means writing brittle, bespoke glue for every pair. Ten agents that all need to talk to each other is a combinatorial mess of one-off integrations. That's the same wall the software industry hit before REST and OpenAPI standardized how services talk.
A2A solves the horizontal problem: agent-to-agent communication. It lets a marketing agent hand a task to a specialist SEO agent it has never met, get structured output back, and move on — without either team coordinating a custom integration first. If you're weighing whether this complexity is worth it, our business guide to agentic AI frames the trade-offs before you commit engineering time.
How does the A2A protocol work?
A2A runs on boring, proven web technology on purpose: HTTP, JSON-RPC 2.0, and Server-Sent Events (SSE) for streaming. No exotic runtime, no proprietary transport. That's a deliberate choice to make adoption cheap. Four concepts carry most of the design.
- Agent Card. A JSON document (typically served at /.well-known/agent-card.json) that advertises what an agent can do — its skills, its endpoint URL, and the authentication it requires. This is how one agent discovers another's capabilities before sending it anything.
- Task. The unit of work. A client agent submits a task to a remote agent, which moves it through a lifecycle: submitted → working → input-required → completed (or failed/canceled). Long-running tasks are first-class, not bolted on.
- Messages and Parts. Communication is broken into typed parts — text, files, or structured data — so agents can exchange more than plain strings.
- Artifacts. The structured outputs a remote agent produces and returns as the task completes.
The most important design decision is that A2A treats agents as opaque. A remote agent exposes what it can do, never how it does it. Its prompts, model choice, internal tools, and memory stay private. That preserves intellectual property, keeps security boundaries clean, and means you can swap an agent's implementation without breaking the agents that call it.
For long-running or asynchronous work, A2A supports SSE streaming and push notifications, so a client agent doesn't have to hold a connection open while a remote agent spends twenty minutes on a task. This is the connective tissue that makes real multi-agent systems practical rather than theoretical.
A2A vs MCP: what's the difference?
This is the question that trips up most technical evaluators, because the two are often mentioned together and sound overlapping. They are not competitors — they solve different axes of the same problem.
MCP (Model Context Protocol) connects an agent to its tools, data, and context — vertical integration. A2A connects an agent to other agents — horizontal integration. A mature system usually uses both: MCP to give each agent its capabilities, A2A to let those agents coordinate.
| Dimension | A2A (Agent-to-Agent) | MCP (Model Context Protocol) |
|---|---|---|
| Primary job | Agent ↔ agent collaboration | Agent ↔ tools, data, context |
| Axis | Horizontal (peer to peer) | Vertical (agent to resources) |
| Unit of exchange | Tasks and artifacts | Tools, resources, prompts |
| What stays hidden | The other agent's internals (opaque) | N/A — you expose specific tools |
| Analogy | Employees delegating to each other | An employee using their apps |
Read them as complementary. You give a single agent hands and eyes with MCP; you let a team of agents divide labor with A2A.
What can you actually build with A2A?
The clearest wins are workflows that span specialties no single agent should own end to end. A few concrete shapes:
- Cross-vendor delegation. Your in-house orchestration agent hands a contract-review task to a specialist legal agent from a third party, gets a structured risk summary back, and never sees the legal agent's internal logic.
- Team-of-specialists pipelines. A planner agent decomposes a request and routes sub-tasks to a research agent, a data agent, and a drafting agent — each independently deployable and independently upgradeable.
- Marketplace-style ecosystems. Agents advertise skills via Agent Cards, and an orchestrator selects the right one at runtime instead of hard-wiring the choice.
How you coordinate those agents — hierarchical supervisor, sequential handoff, or parallel fan-out — is a separate design decision, and one worth getting right early. Our breakdown of multi-agent orchestration patterns maps those choices against real trade-offs. If you're prototyping and want a fast, visual way to wire agents and services together before committing to custom code, a workflow tool is often the shortest path — see our n8n agent-building tutorial for a hands-on start.
What are the limits and open questions?
A2A is young, and a founder or CTO should adopt it with clear eyes rather than hype. Honest caveats:
- The ecosystem is still forming. A standard is only as useful as the agents that speak it. Adoption is growing fast, but you will still meet agents that expose a plain REST or MCP interface and nothing else.
- Trust and identity are hard. Delegating a task to an agent you don't control raises real questions about authentication, authorization, and accountability. A2A leans on standard web auth schemes, but governance of who is allowed to ask what is your responsibility to design.
- Observability across boundaries. When work crosses agent lines, debugging and audit get harder. You need tracing that follows a task across every agent it touches.
- It's not a silver bullet. For a single agent calling a few tools, A2A is overhead you don't need. It earns its keep when you genuinely have multiple, independently owned agents that must cooperate.
The practical takeaway: treat A2A as strategic infrastructure to design toward, not a box to check on day one. Adopt it when the cost of bespoke agent-to-agent glue starts to hurt — which, for teams moving fast, arrives sooner than expected.
How should you decide whether A2A is worth it?
Ask three questions. Do you already run — or will you soon run — more than one agent? Are those agents owned by different teams, vendors, or codebases? Do they need to delegate work to each other rather than just call shared tools? If the answer to all three is yes, an interoperability standard like A2A will save you from rebuilding integration plumbing every quarter. If not, keep it simple and revisit when your agent count grows.
How ILMTEC helps
ILMTEC designs and ships production agentic systems — not demos. We build the individual agents, wire them together with the right interoperability and orchestration choices for your stack, and put the auth, tracing, and governance in place so a multi-agent system is something you can actually operate. If you're evaluating A2A, multi-agent architecture, or how to turn a pile of prototypes into a coordinated network, our AI applications and agents practice delivers it in fixed six-week cycles. Talk to us for an A2A interoperability briefing and an integration consult — we'll map your current agents against where the standard genuinely pays off.
