Tech Talk · 6 essays
AI Native
Agents, evals and the harness around them: putting AI to work inside the data stack, safely.
6 essays
- Data models agents can trustAnalysts fill gaps in a data model with tribal knowledge; AI agents fill them with confident guesses. Four ideas, with real Salesforce examples, for models an agent can answer from correctly.
- Certifying data models with an agent in the loopA production pattern for deciding whether a dbt model is fit for finance reporting: deterministic gates first, sandboxed tests against production data, ticket traceability, and a coding agent used only for triage.
- Turning regulatory PDFs into agent-ready packsModels are bad at 60-page government PDFs and good at well-shaped data. A recipe for converting one into the other, worked through on the public US product-safety eFiling documentation.
- Evals for analytics agents: grading answers against the ledgerIf an AI answers finance questions, its accuracy should be a number you can report. How to build a golden question set, compute ground truth from certified models, score numeric and tabular answers, and catch regressions in CI.
- Five long bets on AI, data and financial infrastructureWhere I'm putting time and capital over the next decade, the signal I watch for each bet, and what would prove me wrong.
- The semantic layer is the API for AIText-to-SQL demos well and fails quietly on finance data. Governed metrics, declared grain and semi-additive measures are what make an analytics agent trustworthy, and the semantic layer is how you hand them over.
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