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Multi agent rag orchestration

Skill Goodnight77/rag-skills/skills/rag-agents/multi-agent-rag-orchestration

Coordinate planner and specialist agents to decompose, retrieve, and synthesize complex RAG answers.From its SKILL.md

Install
npx -y skills add Goodnight77/rag-skills --skill multi-agent-rag-orchestration

Assembled from the repository path, not quoted from the project. Check it against their README if it does not work.

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SKILL.md

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Overview

Multi-agent orchestration splits a complex question across specialized agents, such as a planner, one or more retrievers, and a synthesizer. Each agent owns a narrow job, and a coordinator sequences them and merges the results into a single grounded answer.

Problem Statement

Some questions require several independent lookups, cross-source reasoning, or distinct skills that a single prompt handles poorly. Cramming everything into one agent produces long, unfocused prompts and answers that quietly drop parts of the question.

Key Concepts

  • Planner: Decomposes the query into ordered or parallel subtasks.
  • Specialist agents: Narrowly scoped workers, such as per-source retrievers.
  • Coordinator: Sequences agents, handles failures, and enforces limits.
  • Synthesizer: Merges partial results into one grounded, cited answer.

Implementation Guide

Step 1: Decompose the Query

Have a planner break the question into subtasks that can be handled independently, with explicit dependencies where order matters.

Why: Clear decomposition prevents subtasks from overlapping or dropping parts of the original question.

Step 2: Assign Narrow Specialist Agents

Give each subtask to an agent with a single responsibility and only the tools it needs.

Why: Narrow scope keeps prompts short, decisions debuggable, and behavior predictable.

Step 3: Synthesize and Attribute

Merge partial answers in a synthesis step that resolves conflicts and preserves source citations.

Why: A dedicated synthesis step avoids contradictory fragments and keeps the final answer traceable.

For a worked pattern, see the Multi-Agent RAG example and the LangGraph multi-agent guide.

When to Use This Skill

  • Questions need several independent lookups or cross-source reasoning
  • Subtasks require distinct skills or tools that one agent handles poorly
  • You need parallelism across retrievers to cut wall-clock latency

When NOT to Use This Skill

  • Single-agent routing already answers the query well
  • The added coordination cost and latency are not justified
  • Subtasks are tightly coupled and cannot be cleanly separated

Related Skills

Metrics & Success Criteria

  • Decomposition Quality: Subtasks fully cover the original question
  • Answer Completeness: Multi-part questions are answered in full
  • Latency: Parallel agents keep end-to-end time acceptable
  • Traceability: Each answer segment maps back to a source

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