# Agents-Concerto: Orchestrating Claude Code for PRs

> Source: <https://promptcube3.com/en/threads/3729/>
> Published: 2026-07-26 15:02:09+00:00

# Agents-Concerto: Orchestrating Claude Code for PRs

To fix this, I built agents-concerto. It isn't a standalone app, but an orchestration layer for [Claude](/en/tags/claude/) Code. Think of it as a conductor and a set of players; the conductor manages the flow but never touches an instrument, ensuring the final merge always stays in human hands.

The system relies on a strict set of constraints to maintain quality:

**Two-party authority:** No agent that writes code is allowed to approve or merge it.**Worktree isolation:** Every agent operates in its own git worktree to avoid messing up the local checkout.**No auto-merge:** The process stops exactly at "PR ready."**Model by complexity:** The model isn't tied to a role; it's picked based on the complexity tier of the task.

## The Logic of Acceptance Criteria

The biggest shift in my AI workflow was treating acceptance criteria as test specs rather than human notes. Using a Given-When-Then format (Given `X`

, when `Y`

, then `Z`

), the system ensures that every requirement has an observable result.

This criterion travels through the pipeline: it's defined during the shaping phase, converted into a test during implementation, and verified by the reviewer. Because the "then" must be observable, it prevents the AI from cheating by asserting on internal state.

## The Agent Pipeline

I've divided the labor across four distinct roles:

**Orchestrator (Opus):** Handles decomposition and dispatching. It is strictly forbidden from writing application code.**Classifier (Sonnet):** A read-only agent that assigns a complexity tier (`complex`

vs`standard`

) to determine which model handles the task.**Implementer:** This agent uses a "Tidy First" approach—it submits a structural refactor commit first, then a separate behavioral commit. It never mixes the two.**Reviewer (Opus):** Limited to`Read`

,`Grep`

,`Glob`

, and`Bash`

. It has no`Write`

or`Edit`

permissions, meaning it can describe a fix but cannot apply it.

By forcing "outside-in" testing—asserting only on outputs, UI, or API responses—we've eliminated the "green tests, zero confidence" problem.

```
# Example of the lightweight orchestration structure
# CLAUDE.md (Instruction set)
# agents/ (Markdown definitions for the 4 roles)
# scripts/ (Bash wrappers for execution)
```

For those looking for a real-world LLM agent deployment, the full logic is available here:`https://github.com/moruno21/agents-concerto`

[Next AI Agents for Regulated Industries: My Workflow →](/en/threads/3719/)