multi-agent-patterns

# Multi-Agent Patterns Skill Master the architecture of distributed AI systems. This skill focuses on overcoming context window limitations through strategic task distribution and context isolation. ## Core Patterns ### 1. The Orchestrator (Supervisor) Centralized control where a lead agent manages delegation and quality gates. - **Best For**: Complex, non-linear projects requiring high-level synthesis. - **Mechanism**: The supervisor decomposes the goal, dispatches tasks, and reviews results before final delivery. ### 2. The Swarm (Peer-to-Peer) Decentralized collaboration with direct-handoff mechanisms. - **Best For**: Sequential or highly flexible workflows where tasks follow a natural chain. - **Mechanism**: Agents pass the state directly to the next relevant specialist without a central bottle-neck. ### 3. Hierarchical Layers Multi-tiered structures for large-scale enterprise automation. - **Strategic Layer**: High-level planning and policy. - **Tactical Layer**: Coordination and sub-project management. - **Operational Layer**: Low-level task execution. ## Technical Protocols ### Context Isolation Sub-agents exist primarily to keep context clean. Never inherit the parent's full history unless strictly necessary. Provide "Need-to-Know" context only. ### State Handoff Prevent the "telephone game" by using structured data for handoffs. Explicitly state: - **Input**: The exact data received. - **Transformation**: What the agent did. - **Output**: The clean, synthesized result. ### Token Economics Partitioning is an economic decision. Only split tasks when the context savings exceed the coordination overhead (supervisor prompts + aggregation cost). ## When to Activate - Architecting a multi-agent system or "Swarm". - Designing delegation logic for complex workflows. - Reducing "context poisoning" in single-agent environments. --- *Inspired by the Agent Skills for Context Engineering collection.*