Agent to agent communication: how AI agents talk to each other
Agent to agent communication is how autonomous AI agents exchange tasks, messages, and results with each other. Under every framework and protocol it comes down to four mechanisms: synchronous HTTP calls, streamed responses, events pushed over webhooks, and message queues. Protocols like A2A standardize how agents use them.
Why agents need to talk to each other
A single agent with a good set of tools goes a long way, but real systems quickly split work across specialists: one agent triages a support ticket, another digs through billing history, a third drafts the reply. Those agents are often built by different teams, on different frameworks, running in different clouds. They can't share memory or a process, so they need a way to hand work to each other and hear back when it's done.
That's a distributed systems problem, and it has the same shape whether the nodes are microservices or agents. What's new is the workload: agent tasks are slow (an agent might work for minutes or hours), unpredictable in duration, and often need a human or another agent to chime in midway. That combination is why the communication mechanism you pick matters more than it does for a typical API call.
The four mechanisms
Request/response. One agent sends an HTTP request to another and waits for the answer. This is the simplest option and the right one when tasks finish in seconds. It falls apart as tasks get longer: connections time out, retries duplicate work, and the calling agent burns resources waiting.
Streaming. The receiving agent holds the connection open and streams progress over Server-Sent Events. Good for a user watching an agent think, less good agent-to-agent: someone still has to keep that connection alive, and a dropped stream means reconnecting and resuming.
Push over webhooks. The calling agent hands over a URL, disconnects, and gets an HTTP POST when there's news. This is how long-running work between agents scales, because neither side holds a connection and the caller can be a serverless function that spins up only when the result arrives. It's also the model A2A adopted for async tasks, covered in how A2A push notifications work.
Queues. A message broker sits between agents and buffers the traffic. Queues shine inside one organization's infrastructure, where every agent can reach the broker. They don't cross organizational boundaries well, which is exactly where agent interoperability is heading; you can't ask a partner company to consume from your Kafka cluster.
The protocols on top
Mechanisms move bytes; protocols make agents intelligible to each other. Two matter right now.
A2A (Agent2Agent) is the open standard for communication between agents, introduced by Google in April 2025 and now governed by the Linux Foundation. Each agent publishes an agent card describing what it can do, and other agents send it tasks over JSON-RPC, gRPC, or REST. A task moves through a defined lifecycle (submitted, working, completed, failed), and results come back synchronously, over a stream, or as push notifications, which are webhooks under a different name.
MCP (Model Context Protocol) is often mentioned in the same breath, but it solves a different problem: connecting one agent to its tools and data. The line between them is thinner than the marketing suggests, and A2A vs MCP breaks down when each applies.
Neither protocol replaces the mechanisms above. A2A explicitly builds on all three of its delivery modes, and MCP is adding webhook delivery for async task results. The protocols are converging on the same answer: for anything long-running, push beats holding a connection.
Async is where it gets hard
The awkward truth is that the async mode, the one long-running agent work actually needs, is the least specified part of every protocol. A2A defines the shape of a push notification but says nothing about retries when the receiver is down, signatures to prove who sent it, deduplication, or delivery observability. Those problems have well-known answers in the webhook world, and every team building asynchronous agent communication ends up rebuilding them.
That's the gap [Svix]https://www.svix.com/?utm_source=resources&utm_medium=content) fills: it's the webhook sending infrastructure, so an agent platform gets retries with exponential backoff, signing, and per-receiver delivery logs without building any of it. The agents stay focused on the work; the delivery layer is someone else's job.
Frequently asked questions
How do AI agents communicate with each other?
Over standard web infrastructure: synchronous HTTP request/response for quick tasks, Server-Sent Events for streaming progress, webhooks for pushing results of long-running work, and message queues inside a single organization. Protocols like A2A standardize the message formats and task lifecycle on top of these mechanisms.
What protocol do AI agents use to talk to each other?
A2A (Agent2Agent) is the emerging standard, introduced by Google and governed by the Linux Foundation. It defines agent discovery via agent cards, a task lifecycle, and three delivery modes: synchronous responses, streaming, and push notifications. MCP is complementary; it connects an agent to tools rather than to other agents.
Why do agents use webhooks instead of keeping a connection open?
Agent tasks can run for minutes or hours, and holding a connection that long wastes resources and breaks on any network hiccup. With a webhook the caller registers a URL and disconnects; the working agent POSTs the result when it is ready. The tradeoff is that someone must handle retries, signing, and delivery tracking.
Ready to send webhooks?
Svix handles signing, retries, rate limiting, and delivery observability for the webhooks you send to your users, so your team can stay focused on your product.
Start sending webhooks with Svix or read the build vs. buy analysis