Agentic Analytics: Validation and Context
The advanced layer of agentic analytics: making AI answers you can trust in production. See how context and semantic layers keep metrics consistent across a team, how self-repairing loops catch drift, and how validation tells you when a number is right. Live demos on real data, a take-home each session. It maps straight to our advanced course on reliable, production-grade agentic analytics.
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Wed Aug 5·5:00 PM UTC
Build the Context That Makes AI Data Agents Reliable
Ask an AI the same data question five times and you can get five answers. That is not a model problem, it is a context problem: the agent guesses your metric's meaning differently each run. This session is why agents drift and how one clear definition makes them reliable. You write the contract that stops the drift and watch the answers converge on a single number your whole team can trust.
You'll learn from
Shane Butler
Co-founder, AI Analyst Lab
Sravya Madipalli
Senior DS Leader (Ex-Microsoft)
Hai Guan
Head of Data at Ontra, Ex-LinkedIn
Wed Aug 12·5:00 PM UTC
Build a Semantic Layer So AI Defines Your Metrics
Everyone on your team defines retention differently, and AI makes that worse. Ask three people, or three chat sessions, and you get three numbers, all confidently wrong. The fix is one place that says what each metric means and how tables connect: a semantic layer. Once it exists, AI stops guessing and computes your metrics your way, every time. This session builds a starter one live.
You'll learn from
Shane Butler
Co-founder, AI Analyst Lab
Sravya Madipalli
Senior DS Leader (Ex-Microsoft)
Hai Guan
Head of Data at Ontra, Ex-LinkedIn
Wed Sep 9·5:00 PM UTC
Build a Self-Repairing Context Loop for AI Data Agents
Most AI analysis quietly rots: a metric gets redefined, the agent keeps answering from stale context, and nobody notices until the number is wrong. The fix is not more prompting. It is a loop where the agent detects the drift, repairs its own definitions in the repo, and re-runs until variance collapses. We build that loop live, so you watch the agent catch itself and land on one honest answer.
You'll learn from
Shane Butler
Co-founder, AI Analyst Lab
Sravya Madipalli
Senior DS Leader (Ex-Microsoft)
Hai Guan
Head of Data at Ontra, Ex-LinkedIn
Wed Oct 14·5:00 PM UTC
Trust Your AI Analytics: Know When the Number Is Right
AI output looks the same whether it is right or wrong. It is fluent and confident either way, and that is the trap. Getting an answer stopped being the hard part. The hard part is knowing whether to trust it before it lands in a deck or a decision. This session is the quick checks: spot the confident wrong result, run tests that need no answer key, and know when a number is good enough to act on.
You'll learn from
Shane Butler
Co-founder, AI Analyst Lab
Sravya Madipalli
Senior DS Leader (Ex-Microsoft)
Hai Guan
Head of Data at Ontra, Ex-LinkedIn