Chorus overview

Coding agents are brilliant at the keystroke and lost at the project. Ask one to write a function and it shines; ask it to carry a whole feature — pin down the requirement, plan it, split the work, build it, check it — and you are back to babysitting prompts and re-explaining context every session. The model keeps getting smarter, but the work around the model stays manual.

Chorus is the layer that closes that gap. It is an agent harness: the structure around the model that takes a team of agents and humans from a rough idea all the way to shipped work. The model is the brain; Chorus is everything else. You don’t drive the agents keystroke by keystroke — they propose, and you verify.

The reversed conversation

Most tools work one way: you prompt, the AI responds, and you keep prompting until the output is right. Chorus reverses that.

You drop in a rough idea — say, “the auth module needs refactoring.” Instead of guessing, the agent comes back with questions: which providers, OAuth or SAML? Once the requirement is pinned down it hands you a plan — a short spec and a handful of tasks — and waits for your yes. Only then does it write code, and when it’s done it shows you the tests passing rather than a wall of diffs to trust on faith.

Your job shifts from writing prompts to reviewing proposals. You approve the plan while it is still cheap to change, and you catch a wrong turn at the proposal instead of after the code is written. That is the whole idea: AI proposes, humans verify.

How the work flows

Every idea travels the same path, and nothing moves to the next stage without a clear handoff:

Idea → Proposal → Document + Task → Execute → Verify → Done

You write down an idea. An agent asks a few clarifying questions, then turns the answers into a proposal — a short spec plus the tasks needed to build it, with their dependencies worked out. You review and approve. Agents then pick up the tasks, working in parallel wherever the plan allows, and report as they go. Before anything is called done, the work is checked against what the proposal asked for. You stay in the loop at each handoff without having to drive every step.

The AI-DLC workflow guide walks this path end to end.

Who does the work

You are the reviewer. The doing is split across agents with different jobs and permissions, so each one only touches what its role allows:

• The PM agent turns ideas into proposals — clarifying requirements, writing the spec, and breaking the work into tasks.
• Developer agents pick up those tasks, write the code, report progress, and submit the result for review. Several can work at once on independent tasks.
• The admin agent governs the project — creating it, approving proposals, and verifying finished work.

You can run these on Claude Code, Codex, OpenClaw, or any MCP-compatible agent — see the agent platforms reference for what’s supported.

What you get

Chorus is the connective tissue that makes this collaboration hold together:

• You can see what’s happening. Every agent’s activity is visible in real time — which agent, and which sub-agent, is working on which task.
• You bring your own agent. There is no lock-in to one vendor or model; any MCP-compatible agent can join.
• You own the whole thing. Chorus is open source under AGPL-3.0 and runs on your own infrastructure.

Where to go next

New here? Get started installs Chorus and connects your first agent. From there, the AI-DLC workflow follows an idea from capture to a verified result, and the reference explains the lifecycle stages, how agents and humans collaborate, and the agent platforms Chorus supports. Running it yourself? See deploy & self-host.