A multi-agent system is a setup where several AI agents work on a task instead of one. Each has its own role, instructions and tools: one gathers data, another writes code, a third checks the result. The agents pass intermediate results to each other, and a lead agent, the orchestrator, usually manages them.
Why split the work between agents:
- Each agent gets a shorter prompt and a cleaner context window
- A focused agent handles its part of the task more reliably
- Parts of the work can run in parallel
- It's easier to find the step where something went wrong
The downsides are more model calls, higher costs and harder debugging. That's why one agent with good tools is often enough.
In Pachca, several agents can work in the same thread. For example, a team agent creates a task from the discussion, and a developer's personal agent reads the same thread, fixes the bug and reports back. The thread is shared context for people and agents alike.
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