autonomous-agent-readiness

# Autonomous Agent Readiness Assessment

Evaluate a codebase against proven patterns for autonomous agent execution and provide tailored recommendations.

## Core Philosophy

Most agent failures are system design failures, not model failures. An agent that requires human approval at every step or depends on a developer's laptop being open is not autonomous. Autonomy is an infrastructure decision.

## Assessment Workflow

### Phase 1: Discovery

Gather information about the project's current state:

1. **Examine project structure**
   - Look for CI/CD configuration (`.github/workflows/`, `Jenkinsfile`, `.gitlab-ci.yml`)
   - Check for containerization (`Dockerfile`, `docker-compose.yml`, `devcontainer.json`)
   - Identify test infrastructure (`tests/`, `__tests__/`, test config files)
   - Find environment management (`.env.example`, `requirements.txt`, `package.json`)

2. **Review development workflow**
   - Read contributing guidelines, README, or developer docs
   - Check for sandbox/isolation patterns
   - Look for database setup scripts or fixtures
   - Identify how dependencies are managed

3. **Assess current automation**
   - Review existing CI/CD pipelines
   - Check for automated testing patterns
   - Look for environment provisioning scripts
   - Identify cleanup/teardown procedures

### Phase 2: Evaluate Against Principles

Score the project (0-3) on each dimension. See `references/assessment-criteria.md` for detailed rubrics.

| Dimension | What to Look For |
|-----------|------------------|
| Sandbox Isolation | Ephemeral environments, container support, clean state per run |
| Database Independence | Local DB setup, migrations in code, no external DB dependencies |
| Environment Reproducibility | Explicit dependencies, no hidden state, deterministic setup |
| Session Independence | Remote execution capability, no user session dependencies |
| Outcome-Oriented Design | Clear acceptance criteria, minimal procedural coupling |
| Direct Interfaces | CLI-fi