How do AI agents talk to each other?
Common ways agents communicate
The simplest method is a shared message log. Each agent reads recent messages, adds its own reply and waits for its next turn. A queue works well when agents handle separate jobs and pass results along. Some systems let one agent call another as if it were a tool.
Shared formats matter. If every agent uses the same fields for task, status and result, they can read each other's output without confusion. Structured output like JSON helps a lot.
- Shared message logs for simple back-and-forth
- Queues for background work
- Tool calls for one agent asking another for help
- Structured JSON for predictable handoffs
Keeping multiple agents organized
A coordinator agent or a simple script often assigns work and decides when a task is done. Without one, agents can repeat each other or wait forever. Set a maximum number of turns so conversations cannot loop.
Pass each agent only the context it needs. Sending the full history to every agent raises token costs and can confuse the replies.
- Assign one owner for each task
- Cap turns and retries
- Pass only relevant context
- Log every handoff for debugging
Common mistakes
- Letting agents message each other with no turn limit, which can create endless loops.
- Sending the full conversation history to every agent, which raises costs and slows replies.
- Skipping logs of agent messages, which makes failures much harder to trace later.
Related questions
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