multi-agent-patterns

Design multi-agent systems with coordination, context isolation, and failure handling patterns.

Updated Aug 23, 2026
One-click install
npx skills add https://github.com/monmacllcapp/skill-forks --skill multi-agent-patterns-monmacllcapp
Or copy as Structured Prompt for Agent
Please help me install this Agent Skill.
Skill: multi-agent-patterns
Source: https://github.com/monmacllcapp/skill-forks/tree/main/skills/context-engineering/skills/multi-agent-patterns
Command: npx skills add https://github.com/monmacllcapp/skill-forks --skill multi-agent-patterns-monmacllcapp

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes scripts (resource) and references (resource) components.

What problem does it solve?

This Skill addresses the limitations of single AI agents by enabling the design and implementation of sophisticated multi-agent systems, allowing for complex problem-solving through distributed intelligence and context management.

Core Features & Use Cases

  • Architectural Patterns: Implements Supervisor/Orchestrator, Peer-to-Peer/Swarm, and Hierarchical patterns.
  • Context Isolation: Manages context partitioning to overcome single-agent limitations.
  • Coordination & Consensus: Provides mechanisms for inter-agent communication, handoffs, and decision-making.
  • Failure Handling: Includes strategies for managing agent failures and ensuring system resilience.
  • Use Case: Design a system where a supervisor agent delegates research tasks to specialized researcher agents, which then pass findings to an analysis agent, and finally to a writer agent for report generation.

Quick Start

Use the multi-agent-patterns skill to design a supervisor/orchestrator system for a research task.

Frequently Asked Questions about multi-agent-patterns

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I design a multi-agent system for complex task decomposition?

Design a multi-agent system using supervisor, peer-to-peer, or hierarchical coordination patterns to decompose complex tasks and enable distributed intelligence across specialized agents.

What are the common architectural patterns for AI agent orchestration?

AI agent orchestration commonly uses Supervisor/Orchestrator, Peer-to-Peer/Swarm, and Hierarchical architectural patterns to manage coordination, inter-agent communication, and task handoffs.

How does context isolation improve multi-agent coordination?

Context isolation improves multi-agent coordination by partitioning context across agents, overcoming single-agent limitations and optimizing token economics during distributed processing.

How do you handle failure and ensure resilience in distributed AI systems?

Handle failure in distributed AI systems by implementing architectural strategies for managing agent failures, ensuring system resilience, and maintaining coordination during complex task execution.

When should I use a supervisor orchestrator instead of a peer-to-peer swarm?

Use a supervisor orchestrator when central delegation to specialized agents is needed, and use peer-to-peer swarm patterns when autonomous, decentralized coordination suits the distributed AI task better.

What is the best way to structure a research pipeline using multiple AI agents?

Structure a research pipeline using a hierarchical multi-agent architecture where a supervisor delegates tasks to researcher agents, passing findings to analysis and writer agents for final report generation.