Build the Context That Makes AI Data Agents Reliable

Hosted by Shane Butler, Sravya Madipalli, and Hai Guan

Wed, Aug 5, 2026

5:00 PM UTC (1 hour)

Virtual (Zoom)

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Automate AI Evals with Claude Code
Shane Butler
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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

Co-founder, AI Analyst Lab

Shane Butler is a Co-Founder of the AI Analyst Lab. Previously he led evaluation strategy for AI product development in the legal tech domain. He has more than ten years of experience in product data science and causal inference, with prior roles at Stripe, Nextdoor, and PwC. Shane is also the co-host of the AI podcast Data Neighbor, where he interviews product, data, and engineering leaders who are pioneering the next generation of data science and analytics in an AI-driven landscape.

Sravya Madipalli

Senior DS Leader (Ex-Microsoft)

Sravya Madipalli is a Senior Manager of Data Science with 14+ years of experience helping teams make better decisions with data. She has built and led data science and product analytics teams at Microsoft, eBay, Nextdoor, and Superhuman (prev. Grammarly), working closely with product, engineering, marketing, and leadership. Her expertise spans experimentation, metrics design, modeling, analytics, and translating complex user behavior into clear, actionable insights.

Hai Guan

Head of Data at Ontra, Ex-LinkedIn

Hai Guan leads the data organization at Ontra, the leading legal tech AI solutions for private markets. He previously led Data Science & Analytics at LinkedIn, Nextdoor, Pinterest, and Meta. He's spent a decade teaching product development teams how to ask questions that actually drive decisions—and now teaches how to combine that judgment with AI to move 10x faster.

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