# Provenance and evidence

Essays and notes tagged Provenance and evidence.

- [Self-describing knowledge: why the Open Knowledge Format matters, and what enterprises must add](https://muthub.org/writing/self-describing-knowledge-open-knowledge-format.md) (Sep 15, 2026): In June, Google Cloud published the Open Knowledge Format, and July's v0.2 added provenance, verification and freshness. It is the right foundation. Regulated enterprises still need to add ownership, classification and a policy for how much trust an agent needs before it acts.
- [Passive capture: knowledge that writes itself, with humans who approve it](https://muthub.org/writing/passive-capture.md) (Aug 18, 2026): Documentation fails because it is a second job. The knowledge we need is already produced as a by-product of work: commits, tickets, incidents, reviews and decisions. Capture it passively, distill it, and keep a human approval gate before anything becomes trusted.
- [The AI-native SDLC: when typing code stops being the bottleneck](https://muthub.org/writing/the-ai-native-sdlc.md) (Jul 28, 2026): AI has made writing code cheap. Verification, trusted context and human judgment are now the scarce resources, and teams that redesign their lifecycle around them will pull away from teams that only add assistants.
- [Capture the why: the knowledge layer enterprise agents are missing](https://muthub.org/writing/capture-the-why.md) (Jul 14, 2026): Most enterprise AI pilots hit the same wall: systems record what happened, but the reasoning behind decisions evaporates. Durable, governed context about the why is the real foundation for useful agents.
- [If you can't prove why an AI wrote it, you can't ship it](https://muthub.org/writing/if-you-cant-prove-why-an-ai-wrote-it.md) (Feb 24, 2026): Shipping AI-generated code without recording where it came from is unmanaged risk. Regulated teams need deterministic provenance for non-deterministic models: line-level attribution, Git-native session history and AI bills of materials.
- [Spec-driven development for regulated industries: audit the contract, not every line](https://muthub.org/writing/spec-driven-development-for-regulated-industries.md) (Oct 21, 2025): For complex work in regulated sectors, prompt-to-code cannot pass an audit. Making the specification the contract an AI must fulfil can, provided the spec stays anchored to the code and machines check conformance.
- [From MLOps to AI engineering: what industrializing ML still teaches us](https://muthub.org/writing/from-mlops-to-ai-engineering.md) (Nov 19, 2024): A trained model is the start of the work, not the end. The lessons of industrializing machine learning carry straight into generative and agentic AI, which is why AI engineering has to unify DataOps, MLOps and DevOps.
- [Living documentation and the human trust gap](https://muthub.org/writing/living-documentation-trust-gap.md) (Nov 19, 2025): Docs that regenerate on every commit describe what code does. The harder, more valuable problem is capturing why it exists.
- [Closing the loop: agents that verify their own work](https://muthub.org/writing/closing-the-loop.md) (Aug 13, 2025): An agent that runs the tests, reads the failure and fixes its own work is the line between autonomy and a one-shot tool.
- [Code that validates itself: AI-generated assertions](https://muthub.org/writing/ai-generated-assertions.md) (May 13, 2025): Only about 2.5% of open-source Java repositories use production assertions. LLMs can write them, adding the verification layer AI-written code needs.

---
Muthukrishnan (Muthu) Bommiah · https://muthub.org · Views are my own and do not represent my employer.