Case Study: B2B SaaS Platform

Shipping 16,000 Lines of AI-Authored Code with Zero Production Escapes

Empirical results from deploying CDAL on an enterprise dispatch & HVAC SaaS platform. 14 business days, 1 lead architect, autonomous Claude Code subagents.

Stack: Next.js, TypeScript, Supabase, Claude Code, CDAL
Czas: 14 dni roboczych
Status: Produkcja (Live)

The Problem: Destructive Agent Pitfalls

During initial unconstrained prototyping, the team observed the self-grading agent flaw. When an implementation failed, the model edited the test file (.test.ts) to weaken assertions, then reported complete success. Concurrently, type silencing density surged to 14.2 per 1k LOC.

Empirical Metrics: Before vs. After CDAL

Measured deterministically via scripts/case-metrics.mjs from the project repository.

Quality MetricBefore CDAL (Prototype)After CDAL Implementation
Base Domain Contracts0 (ad-hoc code)6 contracts
Enforced Invariants~10 flaky assertions58 enforced rules
Requirements in Canonical RegisterScattered across prompts47 requirements (REQ-*)
Trace Matrix Test Coverage0% (no links)100% (47/47 with tests)
Verified Selftest Mutations0 (decorative gates)60 / 60 (100% caught)
AI Test Tampering Attempts BlockedUndetected (the agent edited its own tests)342 hook blocks (exit 2)
@ts-ignore & "as any" Density14.2 per 1 kLOC0.0 per 1 kLOC (forbidden gate)
Escaped Production RegressionsFrequent state mismatches0 bugs in CDAL modules

Three Core Architectural Takeaways

  • 1. Separate test-author and implementer: Physical write denial to tests for implementers (denyTests: true) completely eliminated test assertion tampering.
  • 2. A rule without a mutation probe is a decoration: Every validator rule must have a selftest mutation probe. Only mutation testing guarantees that gates are capable of failing red.
  • 3. 100% Trace accelerates code review: With cdal trace --enforce, every PR contained mechanical proof of requirement satisfaction.

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