LLMs can write any code they want, as long as they don’t violate the constraints.

This simple principle represents a fundamental shift in how we think about AI-assisted software development. Today, we’re excited to share our work on Constraint-Driven Development (CDD)—a methodology and technical platform that emerged from a collaboration between GrayBeam Engineering and Claude AI.

The Problem We’re Solving

Every software team faces the same challenge: business rules scattered everywhere. Your frontend validates user input one way, your backend enforces rules differently, your database has its own constraints, and the documentation is—let’s be honest—outdated.

The result? Rules drift out of sync. The frontend allows operations the backend rejects. Manual validation leads to bugs slipping into production. And when you try to use AI to generate code? It’s risky because Large Language Models might violate critical business rules.

Our Solution: Constraints as Guardrails

What if you could extract your business rules once from your existing codebase, encode them as immutable constraints, and then let AI generate code freely—knowing it cannot violate those rules?

That’s exactly what Constraint-Driven Development does:

1. Extract business rules once from your existing codebase
2. Encode as immutable constraints in a validation engine
3. Auto-generate API contracts for all languages
4. LLMs use contracts and cannot bypass validation
5. Single source of truth for all business rules

Technical Architecture

Our platform consists of five key components:

1. Constraint Validation Engine (Elixir/OTP)

Built on Elixir’s robust OTP platform, our engine provides:

• Event-sourced architecture with immutable audit log
• 10 constraint types: Quantitative, Temporal, Invariant, Conditional, Causal, Resource, Authorization, Pattern, Soft, and Probabilistic
• Vector clocks for distributed causal ordering
• Sub-50ms validation latency

2. HTTP Validation API

A language-agnostic REST endpoint that works with any tech stack:

• JSON request/response for event validation
• Contract-based architecture
• Universal compatibility

3. LLM Integration Framework

The magic happens here:

• Constraints become system prompts for AI models
• Auto-generated API contracts (TypeScript, Python, Go, etc.)
• Automatic validation in code generation workflow
• CI/CD pipeline integration

4. Self-Improving Constraints

Our system doesn’t just enforce rules—it evolves them:

• Fitness function measuring correctness, precision, UX, and performance
• LLM-powered constraint evolution
• A/B testing framework
• Automatic deployment of improvements

5. Production Observability

Production-grade from day one:

• OpenTelemetry distributed tracing
• Prometheus metrics
• Structured JSON logging
• Kubernetes health checks

Real-World Results: GridPlay Case Study

We deployed this system on GridPlay, a baseball tournament management platform built with React, Node.js, and Python. We extracted 90 business rules and encoded them as constraints.

The results speak for themselves:

ROI: 15-day implementation with a 2-week payback period.

How It Works

Business Rules → Constraint Engine → HTTP API → Generated Contracts → LLM Code → CI/CD Validation
     ↑                                                                                  ↓
     └──────────────────────── Constraint Evolution (LLM) ←─────────────────────────────┘

The Flow:

1. Extract rules from existing code (React, Node.js, Python, etc.)
2. Import as constraints into validation engine
3. Deploy HTTP validation API
4. Generate language-specific contracts (TypeScript, Python, Go)
5. LLMs use contracts with constraints in prompts
6. Code generation automatically validated
7. CI/CD blocks merges on constraint violations
8. LLMs improve constraints based on real-world fitness metrics

Novel Innovations

We believe these four innovations represent patentable advances:

1. LLM Code Generation with Constraint Guardrails

A method for ensuring AI-generated code adheres to business rules without restricting creative implementation choices.

2. Fitness-Based Constraint Evolution

Self-improving validation system using LLMs to generate, test, and deploy improved constraint variants based on multi-objective fitness functions.

3. Contract-Based Event Validation Architecture

Language-agnostic business rule enforcement via HTTP API with auto-generated client libraries bridging constraints and code.

4. Distributed Constraint Validation with Vector Clocks

Causal ordering for constraint validation across microservices with multi-tenant isolation guarantees.

Why This Matters

Existing solutions—validation libraries, API gateways, rule engines—fall short:

• ❌ No LLM integration
• ❌ Scattered validation logic
• ❌ Manual constraint maintenance
• ❌ No self-improvement

Our solution is the first to combine:

• ✅ LLM integration (generation + evolution)
• ✅ Single source of truth
• ✅ Automatic validation
• ✅ Self-improving via fitness functions
• ✅ Language-agnostic
• ✅ Event-sourced audit trail
• ✅ Production-grade observability

Performance

The system is built for production:

• Validation latency: <50ms P95
• Throughput: 12,000 events/sec
• Health checks: <100ms
• Constraint lookup: <1ms

Implementation Status

Phase 1 (Complete): Core constraint validation platform

• ✅ 10 constraint types implemented
• ✅ Event store with vector clocks
• ✅ GenStage validation pipeline
• ✅ Observability infrastructure
• ✅ 82 tests passing
• ✅ Production-ready

Phase 2 (Ready): HTTP API and contract generation

• Framework complete
• Ready for deployment

Phase 3 (Planned): Full LLM integration

• Prompt engineering complete
• CI/CD templates ready
• Awaiting production deployment

Open Source Plans

We’re committed to open source. Our planned strategy:

• Open source core engine (Apache 2.0 or MIT)
• Hosted SaaS offering
• Enterprise support contracts
• Consulting services

Repository coming soon at: https://github.com/graybeam/constraint-driven-dev

Collaborative Innovation

This project represents a unique collaboration between human expertise and AI capabilities:

• Human contribution: System design, business requirements, domain expertise
• AI contribution: Systematic analysis, implementation, testing, documentation
• Result: Production-ready constraint-driven development platform

Technology Stack

Core: Elixir/OTP, GenStage, ETS/DETS HTTP: Plug + Cowboy Observability: OpenTelemetry, PromEx, JSON logging Storage: DETS (persistent), ETS (cache) Deployment: Kubernetes-ready with health checks

Get Involved

We’re establishing prior art with this public disclosure (October 14, 2025) and preparing for open source release. If you’re interested in constraint-driven development, AI-assisted coding, or the future of software engineering, we’d love to hear from you.

Contact us: Email: origin@graybeam.tech Website: https://graybeam.tech GitHub: https://github.com/graybeam

Conclusion

Constraint-Driven Development represents a new way of thinking about AI in software development. Instead of fearing that AI might violate business rules, we embrace AI’s creative potential while guaranteeing correctness through constraints.

The future of software development isn’t choosing between human precision and AI speed—it’s combining both through smart guardrails.

This blog post establishes prior art for our innovations and serves as the foundation for our open source release. Full technical implementation available in our project repository with complete git history, test suite, and documentation.

Version: 1.0 Classification: Public (Open Source Intent) Timestamp: 2025-10-14T12:00:00Z