A metric definition is not the same thing as evidence that it is ready for an AI client.
Fast access to reusable metrics protects experimentation while visible tests, lineage, and ownership protect trust.
Who you’d be doing this for
“Everyone asks if the agent can use this metric, and I’m still opening four tabs to answer.”
Kamala Desai · Analytics Engineer
Publishes governed metrics for finance and product consumers using BI tools and AI clients.
What is at stake
Analytics engineers currently open separate catalog, lineage, test, and run-history views to judge a metric. You have to weigh a quick path into conversational analytics against evidence people can use to set a trustworthy boundary.
Why it isn’t already fixed
Every obvious fix costs something else. That’s the part you’d have to decide.
- AI access vs metric trust boundaries
- single status vs nuanced evidence
- first-version focus vs cross-surface coherence
- customer experimentation vs governed reuse
Why Fivetran
The dbt Semantic Layer distributes one metric definition across BI tools, notebooks, AI agents, and downstream applications, so readiness evidence must travel with that decision.
Written with these in mind
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This is the setup. The work is inside.
Running it puts you in the room: the full situation and its constraints, stakeholders who push back in their own words, and the decisions that are yours to make. What you produce becomes a Day One Plan — work you can show someone instead of describing.