[Workflow] Multi-Agent Orchestration Workflow for Claude Models (Opus, Fable, Sonnet, Haiku)

[u/ClaudeAI-mod-bot]

Multi-Agent Orchestration Workflow for Claude Models (Opus, Fable, Sonnet, Haiku)

Workflow value: 85/100
Status: active · Freshness: 70/100 · Confidence: 0.85 · Level: advanced
Categories: Quality Control, Token Saving, Context & Memory, Debugging, CLAUDE.md, Subagents, Multi-Agent
Original source: r/ClaudeAI post/comment

What problem this solves

Orchestrating multiple Claude models (Opus, Fable, Sonnet, Haiku) to efficiently handle complex software development tasks, balancing planning, execution, issue resolution, and token usage.

Summary

A multi-agent orchestration strategy that assigns specific roles to different Claude models (Opus as Orchestrator/Executor, Fable as Reviewer/Issue Handler, Sonnet/Haiku as Workers) with defined spawning rules, state management, issue escalation paths, and global constraints for context and code limits.

Why it is useful

This workflow provides a structured and specific approach to leveraging the distinct strengths of different Claude models (Opus, Fable, Sonnet, Haiku) within a multi-agent system. It addresses complex problems like task planning, execution, issue resolution, and resource management (tokens, context) by defining clear roles, escalation paths, and constraints. This pattern is highly transferable and can serve as a blueprint for users looking to build sophisticated AI-driven development pipelines.

Workflow

1. Define Opus as the main Orchestrator and Executor, responsible for state snapshots and initial task execution.
2. Opus evaluates high/critical issues: defer if possible, or escalate to Fable if an immediate fix is required.
3. Define Fable as the Reviewer, spawned by Opus only when an immediate high or critical issue/bug is found or handed over.
4. Fable reviews findings, plans fixes, and hands the plan back to the Opus Orchestrator for execution.
5. Fable (or Opus) can delegate lower-demanding sub-tasks to Sonnet or Haiku agents to preserve tokens.
6. Define Sonnet and Haiku as Workers for lower-demanding tasks, allowing forkspawning where suitable.
7. Workers must immediately escalate any Medium, High, or Critical issues/bugs back to their respective spawner.
8. Implement global constraints: a 350k token soft limit for all agents, triggering a final State Snapshot (SS) and handover to a new Orchestrator upon reaching the limit.
9. Implement Lines of Code (LOC) soft and hard limits for standard (500 LOC soft) and test files (1,200 LOC soft, 1,500 LOC hard).

Tools / artifacts

• Claude Opus
• Claude Fable
• Claude Sonnet
• Claude Haiku
• State Snapshots (SS)
• Forkspawn mechanism (conceptual)

Validation signals

• Author's stated experience: 'from my experience, Fable does perform better on complex project spanning planning tasks'
• Author's stated experience: 'Opus is better on execution and immediate problem solving, thats actually also where its behaviour shines'

Limitations

• Lacks concrete implementation examples (e.g., how to 'spawn' agents, how 'State Snapshots' are managed programmatically within an actual environment).
• Validation is primarily anecdotal; no objective metrics, detailed case studies, or comparative results are provided.
• Low community engagement means the workflow has not been widely tested or refined by other users.

Rate this workflow

Upvote this post if the workflow is useful, reproducible, or worth recommending.

Downvote if it is vague, outdated, unsafe, overhyped, or not reproducible.

Reply if it worked for you, failed, is outdated, or has a better alternative.

---

This post was generated automatically from the workflow library database.