sekirosevillans-sys/awesome-ai-agent-skills/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.