In data analytics, we’re facing a paradox. AI agents can theoretically analyze anything, but without the right foundations, they’re as likely to hallucinate a metric as to calculate it correctly. They can write SQL in seconds, but will it answer the right business question? They promise autonomous insights, but at what cost to trust and accuracy?

These days, everyone is embedding AI chat in their product. But to what end? Does it actually help, or would users rather turn to tools like Claude Code when they need real work done? The real questions are: how can we model our data for agents to reliably consume, and how can we use agents to develop better data models?

After spending the last year exploring where LLMs have genuine leverage in analytics (see my writing on GenBI and Self-Serve BI), I’ve identified three essential pillars that make agentic data modeling actually work: semantics as the shared language both humans and AI need to understand metrics, speed through sub-second analytics that lets you verify numbers before they become decisions, and stewardship with guardrails that guide without constraining. The TL;DR? AI needs structure to understand, humans need speed to verify, and both need boundaries to stay productive. — Read More