multi-agent-patterns

You coordinate multiple agents by partitioning complex tasks across specialized agents to isolate context and enable parallel reasoning. Supervisor, swarm, and hierarchical topologies manage these decomposed subtasks while reducing context pollution.

The best way to structure handoffs and consensus is by using explicit handoff protocols combined with weighted voting and debate mechanisms. These coordination primitives ensure agents converge properly and manage direct agent-to-agent communication effectively.

You add failure recovery to a multi-agent system by implementing checkpointing and circuit breakers. These measures preserve shared state via file-system-backed storage and prevent cascading errors across the agent topology.

Use a hierarchical topology for large projects requiring layered abstraction and clear separation of concerns. Use a peer-to-peer swarm topology for emergent exploration, flexible handoffs, and breadth-first workflows.

Yes, you can use a supervisor and orchestrator pattern for centralized task decomposition, routing, and aggregation. It provides strict control over specialized agents and supports human-in-the-loop oversight for complex workflows.

Yes, context isolation works effectively for parallel research, analysis, and synthesis workflows. It separates specialized agents into focused contexts, enabling parallel reasoning and reducing context pollution during final aggregation.