The Agent Harness for AI-Human Collaboration
Chorus is an agent harness — the infrastructure that wraps around LLM agents to manage session lifecycle, task state, sub-agent orchestration, observability, and failure recovery. It lets multiple AI Agents (PM, Developer, Admin) and humans collaborate on a shared platform through the full workflow from requirements to delivery.
Inspired by the AI-DLC (AI-Driven Development Lifecycle) methodology. Core philosophy: Reversed Conversation — AI proposes, humans verify.
Table of Contents
- Why Agent Harness
- AI-DLC Workflow
- Screenshots
- Features
- Architecture
- Tech Stack
- Getting Started
- Skill Documentation
- Progress
- Documentation
- License
Why Agent Harness
An AI agent is only as reliable as the system around it. The model handles reasoning — but session boundaries, task state, context handoff, sub-agent coordination, and failure recovery all happen outside the model. That surrounding system is the agent harness.
Without a harness, agents drift across long tasks, lose context between sessions, duplicate work, and fail silently. A well-designed harness solves these problems:
| Harness Capability | The Problem It Solves | How Chorus Handles It |
|---|---|---|
| Session Lifecycle | Agents lose track of work across restarts | Every agent gets a persistent session with heartbeats; the plugin auto-creates and closes sessions on spawn/exit |
| Task State Machine | No single source of truth for what's done | Tasks flow through a strict lifecycle — claimed, in progress, submitted, verified — visible to everyone in real time |
| Context Continuity | Fresh context windows start from zero | Each check-in restores the agent's persona, current assignments, and project state so it can resume without re-discovery |
| Sub-Agent Orchestration | Multi-agent work is chaotic without coordination | Lifecycle hooks wire up sub-agents automatically — sessions, context, and unblocked task discovery are handled, not hand-coded |
| Observability | Can't debug what you can't see | Every action is logged with session attribution; Kanban and worker badges show who is doing what, live |
| Failure Recovery | Stuck tasks block the entire pipeline | Idle sessions expire, orphaned tasks are released back to the pool, and any agent can pick them up again |
| Planning & Decomposition | Agents jump into coding without a plan | A PM agent builds a dependency graph of tasks before execution begins — no work starts without an approved plan |
Chorus is not a framework — it doesn't provide building blocks for you to assemble. It is a complete harness with opinionated defaults: lifecycle hooks, ready-to-use MCP tools, role-based access, and a built-in human review loop.
AI-DLC Workflow
Idea ──> Proposal ──> [Document + Task DAG] ──> Execute ──> Verify ──> Done
^ ^ ^ ^ ^ ^
Human PM Agent PM Agent Dev Agent Admin Admin
creates analyzes drafts PRD codes & reviews closes
& plans & tasks reports & verifies
Three Agent roles:
| Role | Responsibility | MCP Tool Prefix |
|---|---|---|
| PM Agent | Analyze Ideas, create Proposals (PRD + task breakdown), manage documents | chorus_pm_* |
| Developer Agent | Claim tasks, write code, report work, submit for verification | chorus_*_task, chorus_report_work |
| Admin Agent | Create projects/Ideas, approve Proposals, verify tasks, manage lifecycle | chorus_admin_* |
All roles share read-only and collaboration tools (chorus_get_*, chorus_checkin, chorus_add_comment, etc.).
Screenshots
Pixel Workspace — Real-time Agent Status
The left panel is a pixel workspace where pixel characters represent each Agent's real-time working status; the right panel shows live Agent terminal output.