Free Lesson

Build the Context That Makes AI Data Agents Reliable

Part of Build Your AI Product Analyst

60 min
Aug 5, 2026 1:00 PM
Virtual (Zoom)

In this video

What you'll learn

See a metric swing wildly across runs

Ask the same data question five times and watch the agent return five different numbers, live.

Write a one-paragraph definition that locks it in

Turn a fuzzy metric into a precise, meaning-only contract the agent has to follow every time.

Watch the answers converge

Feed the agent the definition, rerun the question, and see the numbers snap to the same result.

Set up context so your whole team gets the same number

Store the definition where every agent and teammate reads it, so nobody gets a different answer.

Why this topic matters

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

Shane Butler

Co-founder, AI Analyst Lab

Sravya Madipalli

Sravya Madipalli

Senior DS Leader (Ex-Microsoft)

Hai Guan

Hai Guan

Head of Data at Ontra, Ex-LinkedIn

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