What is the primary benefit of a multi-agent architecture?

The primary benefit is context isolation. By partitioning work across multiple agents, each operates in a clean context window, preventing performance degradation and reasoning errors caused by overcrowded context windows.

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

You should consider a multi-agent approach when tasks decompose into parallel subtasks, when different subtasks require conflicting tool sets, or when you reach the limits of a single context window's reasoning capacity.

What is the 'Telephone Game' problem in AI agent systems?

This occurs when a supervisor agent incorrectly paraphrases or loses detail when summarizing a sub-agent's output. This skill provides patterns, like direct message forwarding, to mitigate this fidelity loss.

How does the Supervisor pattern differ from the Swarm pattern?

The Supervisor pattern uses a central agent to delegate tasks and synthesize results, offering strict control. The Swarm pattern is peer-to-peer, allowing agents to hand off control to one another dynamically, which is better for flexible exploration.

Multi-Agent Architecture Patterns

Name: Multi-Agent Architecture Patterns
Author: muratcankoylan

bymuratcankoylan

•

5,499

データサイエンスとML

Designs and implements robust multi-agent systems using supervisor, swarm, and hierarchical patterns to optimize context management and reasoning.

概要

This skill provides specialized guidance for architecting complex AI systems that distribute workloads across multiple autonomous agents. It treats context isolation as a core design principle, helping developers overcome single-agent context limits by partitioning tasks into distinct context windows. By providing implementation patterns for supervisors, swarms, and hierarchical layers, the skill enables the creation of scalable, parallelized agent systems that avoid common pitfalls like 'the telephone game' effect, context poisoning, and token-heavy bottlenecks.

主な機能

Mitigation techniques for supervisor bottlenecks and agent divergence failure modes
Analysis of token economics and performance trade-offs for production agent systems
Implementation of Supervisor/Orchestrator patterns with context-aware routing
Design of Peer-to-Peer/Swarm architectures for flexible agent handoffs
Context isolation strategies to prevent reasoning degradation and 'lost-in-middle' effects
5,499 GitHub stars

ユースケース

Optimizing agent coordination to reduce latency through parallel execution of independent subtasks
Scaling AI capabilities for complex research tasks that exceed single-context limits
Building production-grade systems where different subtasks require specialized tools or prompts

What are Skills?·How to Install

Install with 🐟 Skill.Fish

npx skillfish add muratcankoylan/agent-skills-for-context-engineering multi-agent-patterns

For use in Claude.ai and ChatGPT

Download Skill

GitHub

概要