Companies that use AI for development
Technical control over repositories and artifacts.
Deterministic Verification
AI-generated code is a hypothesis. It must be verified before it becomes software.
I apply a deterministic verification approach to artifacts produced or accelerated through AI: structure, policy, tests, runtime, dependencies, security, and operational behavior.
AI accelerates. Systems thinking governs.
The new risk
A quickly-generated prototype may look correct but hide security issues, fragile dependencies, wrong assumptions, weak configurations, or untested behaviors.
AI generates. The pipeline verifies. Only what passes the gates becomes shippable.
Verification pipeline
Explicit technical layers that turn a generated artifact into a release candidate.
- Structural integrity
- Release policy
- Automated tests
- Runtime validation in container
- External exposure control
- Dependency analysis
- SBOM
- CVE and known vulnerabilities
- Evidence logging
- Feedback loop to remediation
Methodological foundation
Engineered AI Systems formalized this approach in the working paper "Deterministic Artifact Verification Pipelines for AI-Generated Software Systems".
The paper addresses the asymmetry between the near-zero marginal cost of LLM-based code generation and the persistent cost of verification, proposing a deterministic pipeline to validate AI-generated software artifacts.
Who benefits
Verification matters when generation speed must turn into delivery reliability.
Software teams
Gates and tests to reduce debt and risk.
System integrators
Repeatable method for client projects.
Projects with security requirements
Evidence, policy, dependencies, and controlled runtime.
Verify AI-generated software
A technical audit can clarify what's ready, what's fragile, and what must be fixed before production.