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Reviewed by Muthu · Essay · Last verified Oct 18, 2026 · Drafted Oct 17, 2026 by seed-import/1.0 · Re-check by Oct 17, 2027 · 1 source
Overview
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.1
What it covers
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- Design for the machine consumer first
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- Supply context, and less of it
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- Engineer for bursts and runaway loops
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- Put meaning in the platform
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- Automate governance at machine speed
- Where I disagree with the common advice
- Why an open lakehouse matters here
Bottom line
The goal is a platform where agents can reason and act safely on governed data. Start by finding which of these five principles your platform violates today, and fix that one first.
Sources
- Agent-first data architecture: five principles · published Sep 30, 2025
See also
- Capture the why: the knowledge layer enterprise agents are missing - 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.
- Adopting MCP in a bank: the four risks to solve first - The Model Context Protocol standardizes how agents connect to tools and data. It is silent on whether they should. Four risk areas a regulated institution should resolve before agents get tools.
- Python or the JVM for production enterprise AI? Put the agent where the transaction lives - 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 - 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.
- Data & AI on Public Cloud - A multi-cloud Data & AI strategy and reusable, pre-approved archetypes that made public-cloud onboarding much faster and modernized a legacy data warehouse.
- Controlled Data Movement - A configuration-driven, secure data movement pattern built at USAA to move 25B+ records a day, then carried to Wells Fargo as a bank-wide framework for analytics and AI.
Footnotes
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Agent-first data architecture: five principles, muthub.org, 2025-09-30. ↩