multi-agent-patterns

Coordinate specialized agents with isolation and handoff protocols.

1|Updated Jan 4, 2026

Search Tags:#multi-agent #consensus #swarm #coordination #handoff #orchestrator

Ready to run

npx skills add https://github.com/ChakshuGautam/games --skill multi-agent-patterns-chakshugautam

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: multi-agent-patterns
Source: https://github.com/ChakshuGautam/games/tree/main/.claude/skills/multi-agent-patterns
Command: npx skills add https://github.com/ChakshuGautam/games --skill multi-agent-patterns-chakshugautam

SYSTEM DOCUMENTATION & REQUIREMENTS

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

What problem does it solve?

Multi-agent design enables scalable, collaborative task execution by distributing work across specialized agents with isolated contexts and explicit handoff protocols.

Core Features & Use Cases

Supervisor/Orchestrator, Swarm, and Hierarchical patterns for coordinating complex tasks
Explicit context isolation, state transfer, and convergence checks to prevent drift
Practical guidance on failure handling, consensus, and handoff protocols for robust collaboration

Quick Start

Design a multi-agent pattern for a user task and outline the coordination steps and handoff protocol.

Frequently Asked Questions about multi-agent-patterns

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

FAQPage Schema

What are multi-agent coordination patterns for distributed reasoning?▼

Multi-agent coordination patterns distribute reasoning across specialized agents using supervisor, swarm, and hierarchical models. They provide explicit context isolation and handoff protocols to manage complex research and cross-domain workflows where centralized coordination prevents drift.

How do I design a multi-agent pattern for a complex task?▼

To design a multi-agent pattern, outline the coordination steps and define a handoff protocol for your specific task. Select a supervisor, swarm, or hierarchical structure based on whether your workflow needs centralized control or distributed consensus.

When should I use a swarm pattern versus a hierarchical pattern for agent coordination?▼

Use a hierarchical pattern when subtasks require centralized supervisor control and structured state transfer. Choose a swarm pattern when specialized agents benefit from distributed consensus mechanisms and collaborative convergence without strict top-down orchestration.

How does context isolation prevent drift in multi-agent workflows?▼

Context isolation prevents drift by maintaining explicit boundaries around each specialized agent's state. This ensures that state transfer and convergence checks remain controlled during handoffs, keeping distributed reasoning aligned across cross-domain workflows.

Can I implement robust failure handling for multi-agent handoff protocols?▼

Yes, robust failure handling can be implemented within multi-agent handoff protocols. The coordination framework includes explicit state transfer and convergence checks, ensuring that distributed agents recover gracefully and maintain workflow integrity during failures.

Does multi-agent coordination require a centralized orchestrator?▼

A centralized orchestrator is not strictly required. While supervisor and hierarchical patterns use centralized coordination, swarm patterns enable distributed agents to operate collaboratively using consensus mechanisms without a central orchestrator.