When should I switch from a single agent to a multi-agent system?

Switch when single-agent context limits are exceeded, tasks naturally decompose into subtasks, or when specific subtasks require different tools or system prompts.

Does using multiple agents increase token costs?

Yes, multi-agent architectures typically consume more tokens due to coordination overhead, but provide significantly higher performance for complex reasoning tasks.

Which architectural pattern is best for my project?

Use Supervisor for strict control and planning, Peer-to-Peer/Swarm for flexible exploration, and Hierarchical for large-scale projects with clear organizational layers.

What are the main benefits of multi-agent patterns?

They allow for context isolation, parallel task execution, and specialized agent configuration, which improves reasoning quality and overcomes single-model context limits.

How does this skill solve the 'telephone game' problem?

It implements direct message forwarding protocols that allow sub-agents to pass final results directly to the user, bypassing lossy synthesis by a supervisor agent.

Multi-Agent Architecture Patterns

Name: Multi-Agent Architecture Patterns
Author: goodnight000

bygoodnight000

データサイエンスとML

Designs and implements sophisticated multi-agent architectures to overcome context limitations and handle complex task decomposition.

概要

This skill provides expert guidance on architecting multi-agent systems within Claude Code, focusing on context isolation as a core design principle. It enables developers to implement supervisor, swarm, and hierarchical patterns to scale reasoning capabilities, manage parallel subtasks, and prevent performance degradation caused by context bloat. By applying proven coordination protocols and failure mitigations, it ensures that distributed AI workloads remain coherent, efficient, and high-performing in production environments.

主な機能

Consensus and coordination protocols including weighted voting and debate
Failure mitigation for bottlenecks, divergence, and coordination overhead
Direct pass-through mechanisms to solve supervisor paraphrase errors
Implementation of Supervisor, Peer-to-Peer (Swarm), and Hierarchical patterns
Advanced context isolation strategies to prevent performance degradation
0 GitHub stars

ユースケース

Scaling agentic systems beyond the context limits of a single LLM instance
Building enterprise-grade workflows with specialized layers for strategy and execution
Developing complex research agents that require parallel processing of multiple data sources

概要

主な機能

Consensus and coordination protocols including weighted voting and debate
Failure mitigation for bottlenecks, divergence, and coordination overhead
Direct pass-through mechanisms to solve supervisor paraphrase errors
Implementation of Supervisor, Peer-to-Peer (Swarm), and Hierarchical patterns
Advanced context isolation strategies to prevent performance degradation
0 GitHub stars

ユースケース

Scaling agentic systems beyond the context limits of a single LLM instance
Building enterprise-grade workflows with specialized layers for strategy and execution
Developing complex research agents that require parallel processing of multiple data sources