Multi-Agent Orchestration

SkillDocs & knowledge

Patterns for multi-agent systems and agentic workflows. Use when designing systems where multiple AI agents collaborate, delegate tasks, or follow structured workflows (e.g. Plan-and-Execute).

Available today. Use it from your connected AI after setup.

Connect ahel once, and every AI you use reads what you have installed.

Then ask your AI: use the Multi-Agent Orchestration skill

What this skill tells your AI

The instructions your AI receives, as published by neverinfamous/memory-journal-mcp in skills/multi-agent-orchestration/SKILL.md and read by ahel’s review.

Architectural Patterns

  • Supervisor Pattern: A top-level agent routes tasks to specialized worker agents. In a multi-agent setup, designate a Supervisor agent responsible for delegating sub-tasks to worker agents and synthesizing their final outputs.
  • Plan-and-Execute: One agent plans steps, others execute, and a reviewer validates. For complex tasks, separate the planning phase (using a high-reasoning model) from the execution phase (using faster, specialized models or tools).
  • Tool Use: Agents should have narrowly scoped, deterministic tools to interact with the environment. Worker agents should have the minimum tools required (Principle of Least Privilege). Do not give every agent access to file deletion or database writes.
  • State Management: Use persistent state (e.g., graphs) to track the conversation and execution flow. Use graph-based state machines (e.g., LangGraph) to model agent interactions as predictable state transitions with explicit human-in-the-loop (HITL) checkpoints for destructive actions.

Signals

GitHub stars
20
Forks
5
Last commit
Jul 2026
Advanced
Catalog kind
skill
Gateway key
multi-agent-orchestration-neverinfamous
Source
github.com/neverinfamous/memory-journal-mcp