CURRENT / IN PROGRESS
AI Governance Platform
An open-source platform for governing, evaluating, replaying, and operating AI systems-with reproducibility and operational control treated as first-class concerns.
THE PROBLEM
AI execution is difficult to inspect once it leaves a prototype. The important context-inputs, policies, versions, evaluations, and decisions-needs to remain connected as the system operates.
01 / ARCHITECTURE
- A control-oriented record of execution and its surrounding evidence.
- Evaluation and replay as operational capabilities, not afterthoughts.
- Explicit links between changing artifacts and the outcomes they influence.
02 / DESIGN DECISIONS
- Keep provenance and policy visible in the operational path.
- Model system behaviour as inspectable, versioned evidence.
- Favour vendor-neutral boundaries over a tightly coupled platform surface.
03 / CONSTRAINTS
- Governance has to help teams operate systems rather than merely document them.
- The system must make failure investigation and reproduction practical.
04 / TRADE-OFFS
- More explicit records create more structure to manage, but substantially improve explainability.
- A broad integration boundary gives teams flexibility while demanding careful contract design.
05 / WHAT I LEARNED
- Operational questions should shape architecture early: what happened, why, and can it be replayed?
- Useful governance is a system property, expressed through the control plane and its evidence.