multi-agent-patterns

Multi-agent coordination uses supervisor, swarm, or hierarchical patterns to assign specialized agents to subtasks. A supervisor orchestrates centralized control, swarm enables peer exploration, and hierarchical structures layer planning across agents, allowing parallel reasoning and faster throughput than single-agent systems.

Supervisor patterns use centralized control for task delegation. Swarm patterns enable peer-to-peer exploration for distributed search. Hierarchical patterns layer agents for staged planning and execution. Each suits different workflows: supervisor for coordinated workflows, swarm for exploratory problems, hierarchical for complex reasoning chains.

Multi-agent architectures maintain context isolation with explicit handoffs and state sharing between agents. Each agent works within its own context window, passing only relevant information forward through handoff protocols and convergence checks, enabling specialization without hitting single-agent memory constraints.

Yes. Multi-agent patterns excel at orchestrating data analysis projects requiring web research, data extraction, and reporting. Assign specialized agents to each domain, coordinate through a supervisor or hierarchy, and aggregate results—accelerating delivery while maintaining clean separation of concerns.

Robust multi-agent systems implement consensus mechanisms to validate agent outputs and error handling to recover from failed subtasks. Handoff protocols ensure clean state transitions, convergence checks verify alignment across agents, and framework-specific delegation patterns enable graceful failure and retry strategies.

Multi-agent coordination applies across frameworks through common patterns: supervisor orchestration, swarm protocols, and hierarchical reasoning. Framework-specific references guide implementation details, but the core architectural principles—context isolation, explicit handoffs, and delegation—remain consistent regardless of tooling.