# Data & AI platforms

The data and context foundations agents depend on.

- [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.
- [Agent-first data architecture: five principles](https://muthub.org/writing/agent-first-data-architecture.md) (Sep 30, 2025): Data 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.
- [Python or the JVM for production enterprise AI? Put the agent where the transaction lives](https://muthub.org/writing/python-or-jvm-for-enterprise-ai.md) (Jun 24, 2025): Python is unrivaled for research and prototyping. Production enterprise AI is mostly integration, security and scale, and with JVM agent frameworks now maturing, the runtime that owns the transaction is often the right home for the agent.
- [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.

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