q-agent-harness

Starter kit for building an agent harness that helps AI agents write, test, fix, and ship software more reliably.

This repo is built around three ideas:

1. A map
2. Guardrails
3. Feedback loops

If an agent cannot find the right context, it guesses. If an agent has no boundaries, it makes risky moves. If an agent cannot see what broke, it cannot improve its own work.

What is harness engineering?

Harness engineering is the work of building the environment around an AI agent so it can do useful software work on purpose.

That environment usually includes:

• repo docs and architecture notes
• agent instructions
• tool permissions and approval points
• coding and schema boundaries
• tests and UI validation
• logs, metrics, and traces

The goal is not to write the perfect prompt. The goal is to make good behavior easier for the agent than bad behavior.

The 3-part framework

1. Map

Give the agent a clear model of the system:

• what the product does
• how the repo is organized
• where business rules live
• how modules are allowed to depend on each other
• where to find important scripts, tests, and docs

See docs/repo-structure.md.

2. Guardrails

Make risky behavior harder:

• validate schemas at boundaries
• enforce linting and type checks
• document approved tools and commands
• require human approval for destructive actions
• keep architecture and dependency directions explicit

See docs/starter-checklist.md.

3. Feedback loops

Let the agent see whether its work actually worked:

• unit and integration tests
• UI checks
• logs
• metrics
• traces
• review loops

The faster an agent can observe failure, the faster it can recover.

What is in this repo?

• AGENTS.md: a starter instruction file for agent-facing repositories
• templates/AGENTS-template.md: a copy-paste template
• docs/starter-checklist.md: practical harness checklist
• docs/task-setups.md: how the harness should change for debugging, research, and decisions
• docs/repo-structure.md: sample repo layout and why it matters
• docs/product.md, docs/architecture.md, docs/operations.md: example repo-local context for the WorkspaceHub sample app
• docs/approval-policy.md and docs/architecture-boundaries.md: public examples of guardrails and structural boundaries
• examples/ and tests/: lightweight examples that make validation, approval, verification, and task-specific setups visible
• content-agent-loop: A real-world autonomous content creation loop (research → draft → self-critique → refine) built using loop engineering principles
• demo/: simple dashboard, log, shell-script, and task-matrix assets for showing feedback loops and setup differences

How to use this starter kit

1. Copy the AGENTS.md template into your repo.
2. Create a small docs/ directory that explains your system.
3. Add basic guardrails for validation, boundaries, and approvals.
4. Tighten your feedback loops so the agent can detect and fix problems faster.
5. Change the setup around the agent depending on the job instead of using one generic workflow for everything.