# AI strategy

Essays and notes tagged AI strategy.

- [The cost of unmanaged context](https://muthub.org/writing/the-cost-of-unmanaged-context.md) (Jun 2, 2026): 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.
- [Model is a tool, harness is the strategy](https://muthub.org/writing/model-is-a-tool-harness-is-the-strategy.md) (May 26, 2026): Handing 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.
- [Three architectures for AI coding agents, and how enterprises should choose](https://muthub.org/writing/three-architectures-for-ai-coding-agents.md) (Dec 16, 2025): Platform-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.
- [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 arriving, 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.
- [AI is an amplifier, not a silver bullet](https://muthub.org/writing/ai-is-an-amplifier.md) (Sep 24, 2025): AI magnifies whatever is already in a team. Platform quality, clear policy and updated workflows decide whether that means strength or dysfunction.
- [Build for today's task or tomorrow's platform?](https://muthub.org/writing/build-for-today-or-tomorrow.md) (Sep 22, 2025): The classic architect's dilemma, upgraded for agents: how much to invest in reusable agents versus fast, single-use ones.

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