Muthukrishnan Bommiah · muthub.org
Enterprises budget for compute, models and licenses, but almost none budget for the context those models depend on. Unmanaged context is the largest cost nobody tracks, and every new AI agent adds to it.
Context engineering6 min readHanding an AI agent a production codebase without a harness is like putting a teenager in a sports car with no dual brakes. Deterministic guardrails and sensors, owned as a platform, are what make agents safe to scale.
Governed AI6 min readPlatform-embedded, sandboxed and evolutionary coding agents each earn trust in a different way. Then the editor became a fleet manager. Choose and govern agents by how their work will be verified.
AI-native SDLC6 min readData platforms built for people asking questions will strain when the main consumer is software taking actions. Five principles for designing data platforms where agents are first-class users, and why an open lakehouse helps.
Data & AI platforms6 min readPython is unrivaled for research and prototyping. Production enterprise AI is mostly integration, security and scale, and with JVM agent frameworks now arriving, the runtime that owns the transaction is often the right home for the agent.
Data & AI platforms5 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 readAI magnifies whatever is already in a team. Platform quality, clear policy and updated workflows decide whether that means strength or dysfunction.
Note1 min readThe classic architect's dilemma, upgraded for agents: how much to invest in reusable agents versus fast, single-use ones.
Note1 min read