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Multi-Agent Architecture Patterns

Design systems where several AI agents split work, with clear coordination and handoffs

Source Download skill
Repository
muratcankoylan/Agent-Skills-for-Context-Engineering
Stars
18,282
Licence
MIT
Files
3 files

What it does

When installed, the agent gains guidance on whether a task really needs several cooperating AI agents, each with its own clean context, and if so which coordination pattern to use (supervisor, peer-to-peer swarm, or hierarchical). It explains trade-offs such as higher token costs, the 'telephone game' loss of detail when a supervisor paraphrases workers, and how to avoid error cascades. It also comes with a Python helper file of coordination building blocks (messages, handoffs, consensus voting, circuit-breaker failure handling) that the agent can reuse in code it writes.

Great for

  • Developers designing an AI agent system that may need several specialised agents
  • Architects deciding whether multiple agents are worth their extra cost compared with one agent
  • Teams who want a structured starting point for supervisor or swarm style coordination

Skip it if

  • Simple single-step tasks, where extra agents add token cost and complexity without benefit
  • Production deployments without testing, since the patterns are guidance and the helper code is an illustrative sketch rather than a hardened library
  • Hosted infrastructure or sandbox design, which the skill itself says belongs to other skills

Try asking

  • “Should I use a supervisor agent or a swarm for my research assistant that searches several sources in parallel?”
  • “Help me design a multi-agent system where each specialist agent has its own tools and context.”
  • “Show me how to add a forward_message tool so sub-agent answers reach the user without losing detail.”
  • “Compare a single-agent baseline against a multi-agent setup for my document analysis pipeline.”
Step-by-step guide: how to use Multi-Agent Architecture PatternsInstall, first run, examples, tips and FAQs

Install in 3 steps

  1. 1

    Download the skill

    The ZIP contains the 3 original files, the MIT licence and an attribution note.

    multi-agent-patterns.zip
  2. 2

    Add it to your agent

    Claude Code (all projects)

    mkdir -p ~/.claude/skills && unzip multi-agent-patterns.zip -d ~/.claude/skills/

    Claude Code / Agent SDK (this project only)

    mkdir -p .claude/skills && unzip multi-agent-patterns.zip -d .claude/skills/

    Claude apps (web/desktop): Settings → Capabilities → Skills → Upload skill, and choose the ZIP.

  3. 3

    Ask for the task

    No special command needed — the agent reads the skill's description and loads it when your request matches. Try one of the prompts above.

Get better results

  • Ask the agent to first justify whether multiple agents are needed, using the single-agent baseline comparison the skill describes.
  • Review and test any code from coordination.py before reusing it in production, since it is a teaching-style utility module.
  • State your coordination needs (human oversight, parallel subtasks, exploration) so the agent can pick the matching pattern.

What's inside

  • references/frameworks.md
  • scripts/coordination.py
  • SKILL.md

Redistributed unmodified from muratcankoylan/Agent-Skills-for-Context-Engineering under the MIT licence. The assessment was drafted with AI from the skill's files on 2026-10-29. Skills can instruct your agent to run code — read SKILL.md and any scripts before installing.