Muthukrishnan Bommiah · muthub.org
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.
Context engineering7 min readDocumentation 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.
Context engineering6 min readAI 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.
AI-native SDLC6 min readMost 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.
Data & AI platforms6 min readShipping 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.
Governed AI5 min readFor 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.
AI-native SDLC6 min readA 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.
Data & AI platforms6 min readDocs that regenerate on every commit describe what code does. The harder, more valuable problem is capturing why it exists.
Note1 min readAn agent that runs the tests, reads the failure and fixes its own work is the line between autonomy and a one-shot tool.
Note1 min readOnly about 2.5% of open-source Java repositories use production assertions. LLMs can write them, adding the verification layer AI-written code needs.
Note1 min read