Multi-stage pipeline that separates the AI's probabilistic generation from release decisions. Every artifact goes through checks on structure, policy, tests, runtime, dependencies, security, and operational behavior. Structured evidence produced at each stage flows back to the AI as correction context, creating a deterministic loop between generation and acceptance. System in real-world use on a client project, formalized after the fact in a research paper.
Highlights
- 100+ iterated releases on the production system
- 2,446+ automated tests
- 12 modules of static analysis
- 9 phases of supply chain verification
- Architecture as deterministic oracle between AI and release
- Research paper: Deterministic Artifact Verification Pipelines for AI-Generated Software Systems (Draft v1.0, 2026)
- Manifesto: Software artifacts are hypotheses. They are never trusted. They are verified.
In production. Paper available as draft; preprint server publication in progress.