Multi-Agent Coordination

Simple Explanation

Coordinate multiple agents to work on parts of a problem in parallel, then combine the results. Use semaphores, quotas, and clear roles.

Technical Explanation

Define roles and tasks, assign agents based on capabilities, and use a coordinator to manage fan-out/fan-in with resource limits and idempotency keys. Aggregate outputs deterministically and record provenance.

When to use - Complex tasks that decompose into parallelizable subtasks - Teams of specialized agents with shared context

Key Primitives - Role definitions and task decomposition - Coordinator with concurrency controls - Idempotency cache and provenance tracking - Deterministic aggregation and conflict resolution

Try it - Coordinate agents: samples/17-multi-agent-coordination/coordinate_agents.py - See the README: samples/17-multi-agent-coordination/README.md

Gotchas - Avoid race conditions; use clear ownership and locks - Keep shared context small; pass references not blobs - Handle stragglers with timeouts and partial aggregates