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Stop wasting time on non-deployable AI-generated code. Ship with confidence.

What CI actually verifies (this framework's own rule — no claim without a check): the 28-check validation suite, the MCP server build on Node 20/22, and the dashboard's 14 vitest tests on Node 20/22. An earlier "136/136 tests" badge here referenced a runner that no longer exists in the repo and has been retired.

Dogfood score: the framework run against its own repo scores DRS 27/100 (2026-07-18, ai-framework/reference/bash/drs-calculate.sh) against its own ship threshold of 85. Published as-is: most DRS components measure live-session artifacts (frozen contracts, fresh evidence, integration proofs) that a framework repo does not carry — the score is honest, and low, and that is the point of the tool.

95% of generative AI pilots fail to move to production — MIT/Fortune, 2025

42% of AI projects were abandoned in 2025 (up from 17% in 2024) — S&P Global

This framework exists because AI coding tools are powerful but undisciplined. Without guardrails, they produce impressive demos that never ship.

Run one command. Get a deployability score. Know exactly what to fix.

AI coding assistants (Claude Code, Cursor, Copilot) fail in predictable ways. Instead of asserting frequencies, this repo measures them — on the author's own repos (methodology, pinned commits, raw data, 2026-07-18, one developer, N=19; observational, not causal — read the limitations before quoting):

Earlier versions of this README asserted "68% of sessions", "4.2x per feature", and a before/after results table. No dataset behind those numbers exists; they are retired, not re-derived. What's above is what could actually be measured.

This framework addresses 13 specific failure patterns; the benchmark measures the statically detectable ones.

The before/after table that used to sit here (time-to-deploy, rework rate, breaking changes) is retired — it had no dataset behind it. The measured evidence that exists lives in benchmarks/: what structural discipline demonstrably changes on this author's own repos is real CI (15% → 67%), licensing (38% → 100%), and the absence of mock data in production paths — and what it demonstrably does not automatically fix is README claim hygiene (the disciplined cohort was worse). Session-level outcome deltas remain unmeasured; if that measurement is ever built, it will appear in benchmarks/ with its methodology, not here as a table.

git clone https://github.com/sgharlow/ai-control-framework.git
cd ai-control-framework

# Mac/Linux
./install.sh your-project-path

# Windows PowerShell
./Install.ps1 your-project-path

bash ai-framework/reference/bash/initialize-project.sh

I'm using the AI Control Framework for disciplined development.

MANDATORY: Read these files:
1. CLAUDE.md - Operating instructions
2. ai-framework/templates/code.md - Session state

Run ./ai-framework/scripts/can-i-continue.sh now.

$ ./ai-framework/scripts/check-contracts.sh
✓ Contracts frozen: api/openapi.yaml, db/schema.sql

# Later...
$ ./ai-framework/scripts/check-contracts.sh
✗ CONTRACT VIOLATION DETECTED!
STOP: api/openapi.yaml hash mismatch

# Minute 15
$ ./ai-framework/scripts/detect-mocks.sh
⚠ 2 mocks detected - 15 minutes remaining

# Minute 35
$ ./ai-framework/scripts/detect-mocks.sh
✗ VIOLATION: Mocks detected after 30-minute mark!
Required: Replace with real service calls

$ ./ai-framework/reference/bash/drs-calculate.sh
═══════════════════════════════
DEPLOYABILITY SCORE: 85/100
═══════════════════════════════
✓ Contract Integrity     (8/8)
✓ Behavioral Contracts   (8/8)
✓ Security Validation   (18/18)
✓ Data Integrity        (10/10)
✓ No Mocks               (8/8)
✓ Tests Passing          (7/7)
✓ Integration Evidence  (10/10)
✓ Architecture Stability (7/7)
⚠ Production Readiness   (9/15)
✓ Context Preservation   (8/8)

✅ READY TO DEPLOY - DRS ≥ 85

your-project/
├── CLAUDE.md                    # AI agent instructions
├── ai-framework/
│   ├── specs/                   # 12 specifications
│   ├── reference/
│   │   ├── bash/                # Bash scripts
│   │   └── powershell/          # Windows scripts
│   ├── templates/               # 9 tracking templates
│   └── prompts.md               # 20 ready-to-use prompts
└── ai-framework-mcp-server/     # MCP integration for Claude Code

# Adjust limits in check-scope.sh
MAX_FILES=5      # Files per session
MAX_LINES=200    # Lines per session

# Adjust timeout in detect-mocks.sh
MOCK_TIMEOUT=30  # Minutes

# .github/workflows/ai-control.yml
name: AI Control Framework
on: [push, pull_request]

jobs:
  validate:
    runs-on: ubuntu-latest
    steps:
      - uses: actions/checkout@v2
      - name: Check Contracts
        run: ./ai-framework/scripts/check-contracts.sh
      - name: Check DRS
        run: |
          DRS=$(./ai-framework/scripts/drs-calculate.sh | grep SCORE | cut -d: -f2 | cut -d/ -f1)
          if [ $DRS -lt 70 ]; then exit 1; fi

Q: What if I need more than 5 files? Deploy what you have (DRS 85+), then start a fresh session.

Q: Can I override the mock timeout? No. This is intentional. 30 minutes forces real implementation.

Q: What if contracts must change? Run approve-contract-change.sh with justification. Creates audit trail.

Q: Does this work with all AI assistants? Yes, if they can read project files. Optimized for Claude Code.

We're exploring a hosted dashboard for teams. Planned features:

• 📊 DRS Analytics Dashboard — Track scores over time, identify patterns
• 👥 Team Management — Shared metrics, leaderboards, team insights
• 🔔 Slack/Discord Alerts — Get notified on violations or milestones
• 📈 Session History — Review past sessions, export reports

Interested? Join the discussion and tell us what features matter most to you.

The core framework will always be free and open source.

See CONTRIBUTING.md.

Priority areas: language-specific patterns, CI/CD integrations, success stories.

MIT License - Use freely in personal and commercial projects.

• GitHub Issues
• GitHub Discussions

Without this framework: 70% of AI coding time wasted on non-deployable output.

With this framework: Ship production code in hours, not days.

Stop hoping AI code will work. Start knowing it will deploy.