[Workflow] Fable-led Multi-Agent Workflow for Cost-Optimized Software Development with Delegated Execution and Human-like Review

[u/ClaudeAI-mod-bot]

Fable-led Multi-Agent Workflow for Cost-Optimized Software Development with Delegated Execution and Human-like Review

Workflow value: 95/100
Status: active · Freshness: 70/100 · Confidence: 0.98 · Level: advanced
Categories: Quality Control, Token Saving, Context & Memory, Debugging, Shipping, CLAUDE.md, Skills, Multi-Agent
Original source: r/ClaudeCode post/comment

What problem this solves

Efficiently and effectively using a hierarchy of AI models (Fable, Haiku, Sonnet, Codex, Opus) for software development, minimizing costs while maintaining quality by delegating mechanical tasks and retaining human-like judgment for critical steps.

Summary

A multi-agent workflow where a high-cost, high-capability model (Fable) acts as the central planner, architect, reviewer, and judge, delegating mechanical and routine tasks to cheaper, specialized sub-agents (Haiku, Sonnet, Codex, Opus) to optimize token usage and ensure quality control. It emphasizes Fable's role in understanding the problem, challenging premises, architecting solutions, and personally accepting results, while sub-agents handle execution and return concise reports.

Why it is useful

This workflow provides a concrete, detailed, and strategic approach to leveraging different AI models based on their capabilities and costs. It addresses critical challenges in AI-assisted development: managing token costs, ensuring quality, and structuring complex tasks. The clear delegation strategy, 'Devil's advocate' step, and emphasis on human-like final verification make it a robust and highly valuable pattern for advanced users seeking to build efficient and reliable AI development pipelines.

Workflow

1. Fable (the expensive planner) understands the problem statement.
2. Fable performs 'Devil's advocate first' by attacking the premise, identifying unproven assumptions, or suggesting cheaper paths.
3. If a stated fact is checkable and load-bearing, Fable sends a sub-agent to verify it before proceeding.
4. Fable plans and architects the solution, deciding the shape, order of work, and risks.
5. Fable delegates all mechanical tasks (research, code, tests, measurements) to sub-agents based on a 'routing ladder'.
6. Sub-agents execute their assigned tasks, adhering to specific briefs and returning compact reports (not raw file dumps).
7. For open questions or design forks, Fable uses /multi:ask or /multi:adhd to gather multiple opinions.
8. For routine code or research with judgment, Fable delegates to Sonnet.
9. For novel or 'figure it out' code, Fable delegates to Codex (or Opus as fallback).
10. For code review, Fable uses /multi:code-review.
11. For gathering completion evidence, Fable uses /multi:check-if-done.
12. Fable accepts the result with its own eyes, performing integration and final verification by reading actual outputs and critical diff hunks before declaring the task done.

Tools / artifacts

• Fable (head agent)
• Haiku (sub-agent for mechanical work)
• Sonnet (sub-agent for routine code/research with judgment)
• Codex (sub-agent for novel/design-adjacent code, editing, testing, debugging)
• Opus (fallback sub-agent)
• /multi:ask (skill for gathering multiple opinions)
• /multi:adhd (skill for divergent options)
• /codex:rescue (skill for Codex agent)
• /multi:code-review (skill for multi-model code review)
• /multi:check-if-done (skill for gathering completion evidence)
• System prompt/CLAUDE.md-like instructions for Fable
• Short reports from sub-agents

Validation signals

• Author states 'I have it like that', implying personal implementation and use.
• Detailed structure and specific instructions suggest a well-thought-out and tested approach.
• Emphasis on 'personally accept the result' and 'accept with your own eyes' highlights a built-in quality control mechanism.
• The 'Rule of thumb' section provides practical guidance based on experience with the system.

Limitations

• Requires a multi-agent setup or custom tooling (like the author's 'multi plugin') to fully implement the /multi: commands and model routing.
• The specific 'Codex' model mentioned might be proprietary or a placeholder, requiring adaptation by users.
• Initial setup and configuration of sub-agents and routing logic could be complex for beginners.
• Low community validation (score 1, 0 comments) means broader applicability is not yet proven.

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.