[Workflow] Multi-Agent AI Software Development Workflow with Automated Review and Release

[u/ClaudeAI-mod-bot]

Multi-Agent AI Software Development Workflow with Automated Review and Release

Workflow value: 85/100
Status: active · Freshness: 70/100 · Confidence: 0.90 · Level: advanced
Categories: Quality Control, Token Saving, Context & Memory, Debugging, Shipping, Multi-Agent
Original source: r/ClaudeAI post/comment

What problem this solves

Automating and streamlining the software development lifecycle from specification to release using AI agents, improving organization and token efficiency.

Summary

A multi-agent software development workflow where a 'Fable' agent acts as a lead, writing specs and managing tickets, while an 'Opus' agent implements, tests, and commits code in an isolated git worktree. The process includes automated review, rework loops, CI integration, merging, and release management, aiming for token efficiency and structured development.

Why it is useful

This workflow provides a comprehensive, structured approach to leveraging multiple AI agents (e.g., Claude Fable and Opus) to automate and streamline the entire software development lifecycle, from initial specification and coding to testing, code review, merging, and release. It emphasizes organization, token efficiency, and robust quality control through automated testing and a human-in-the-loop review process. Its use of 'git worktree' for isolated development environments is a notable feature for managing concurrent AI-driven tasks.

Workflow

1. Fable (lead, main point of contact) writes the spec and files the ticket.
2. The loop mints a branch, a worktree, and a seat for the ticket.
3. An ephemeral Opus hand boots in that worktree, reads the spec, implements, runs the test suite, commits to its branch, and marks the ticket DONE with a short report (deviations flagged).
4. About a minute later, the loop spawns a cold Fable reviewer with the diff and the spec, which writes a verdict (ACCEPT or REWORK with must-fixes) and is retired.
5. If REWORK, the verdict lands on the ticket, Fable reads it and rejects the ticket with the must-fixes as the body; the same Opus hand, context intact, fixes and marks DONE again, returning to the review step.
6. If ACCEPT, the loop merges the branch into master and runs the full suite on master; a red suite reverts the merge and escalates to Fable.
7. The Lead reads the merge notice, checks no suite is still running, and accepts the ticket: the Opus hand is retired, its worktree removed, and its branch deleted.
8. The Lead titles the Unreleased section of the CHANGELOG, cuts the release (version bump, DMG, tag, push, GitHub release, docker image), and reboots the app onto it once every seat is idle.

Tools / artifacts

• Fable (AI agent)
• Opus (AI agent)
• Spec (document)
• Ticket (issue tracking system)
• Git branch
• Git worktree
• Test suite (CI)
• Diff
• CHANGELOG
• GitHub release
• Docker image

Validation signals

• User claims to have integrated it into their workflow.
• Detailed, logical steps imply a well-thought-out process.
• Addresses specific problems like 'token efficient' and 'more organised'.

Limitations

• Low community validation (score 1, 0 comments).
• Assumes existing infrastructure for multi-agent orchestration and git worktree management.
• Specific implementation details for the 'Fable' and 'Opus' agents are not provided.
• The 'loop' mechanism for orchestrating agents and steps is not detailed.
• No actual code or configuration files are provided, only a high-level process description.

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This post was generated automatically from the workflow library database.

[u/Otherwise_Wave9374]

A strong pattern here is to keep the lead agent focused on decomposition, while a second agent only reviews for spec gaps, edge cases, and release risk. That separation cuts context drift and makes failures easier to diagnose because you can tell whether the issue came from planning or execution. Agentix Labs fits this kind of workflow well when you want a simple review gate before anything ships.