multi-agent-patterns

Design multi-agent architectures with supervisor, swarm, and hierarchical patterns.

5|Updated Dec 25, 2025

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

Ready to run

npx skills add https://github.com/rohunvora/my-claude-skills --skill multi-agent-patterns-rohunvora

💬 Prompt for ChatGPT / Claude Chat▼

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

SYSTEM DOCUMENTATION & REQUIREMENTS

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

What problem does it solve?

Design multi-agent architectures to distribute work across multiple LLM contexts, enabling capabilities beyond single-agent limits.

Core Features & Use Cases

Pattern 1: Supervisor/Orchestrator
Pattern 2: Peer-to-Peer/Swarm
Pattern 3: Hierarchical
Context Isolation as Design Principle
Consensus mechanisms

Quick Start

Define a supervisor to route tasks and deploy specialized workers; implement a simple swarm with handoffs; set up hierarchical delegation flow.

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 to handle complex tasks?▼

Multi-agent architectures distribute work across specialized LLM contexts using supervisor, peer-to-peer swarm, or hierarchical patterns. Each pattern handles task decomposition, routing, and coordination differently—supervisors centralize decisions, swarms coordinate through handoffs, hierarchical approaches delegate through layers—enabling capabilities beyond single-agent limits across isolated contexts.

What are the main coordination patterns for multiple agents?▼

Three core patterns structure multi-agent coordination: supervisor/orchestrator centralizes routing decisions, peer-to-peer swarm enables autonomous handoffs between agents, and hierarchical delegation chains tasks through levels. Each isolates context, implements consensus mechanisms, and handles synchronization and failures—choose based on whether you need centralized control, autonomous collaboration, or layered authority.

When should I use a multi-agent approach instead of a single agent?▼

Use multi-agent architectures when tasks decompose into parallel subtasks, require specialized tool sets or domain expertise per agent, demand context isolation, or exceed a single agent's processing capacity. Multi-agent designs handle task complexity through explicit coordination protocols and handle failures robustly across distributed contexts.

How do I ensure agents coordinate and reach consensus?▼

Multi-agent patterns implement consensus mechanisms and synchronization protocols within coordination frameworks. Supervisor patterns use centralized decision logic, swarm patterns use peer handoffs, and hierarchical patterns use delegation chains—all require explicit context isolation and failure handling to ensure reliable coordination across agent interactions.

Can I route different task types to specialized agents?▼

Yes. Task decomposition and routing are core features—supervisor/orchestrator patterns route tasks to specialized workers based on tool sets or domain requirements, while swarm and hierarchical patterns distribute work through peer handoffs or delegation chains. Context isolation ensures each agent operates independently with appropriate tool access.

What happens if an agent fails in a multi-agent system?▼

Robust failure handling is built into multi-agent patterns through explicit synchronization and recovery protocols. Supervisors can reroute failed tasks, swarms can reassign work through handoff logic, and hierarchical systems can escalate or retry at delegation boundaries—each pattern requires failure strategies tailored to its coordination model.