Episode 34 — Weimo Liu

When you ask an AI a question about your company’s data, it looks something up before it answers. Most of the time that works. But often it joins the wrong tables and returns an answer that looks completely reasonable and is completely false, and nothing catches it. There is no error message.

This week on Machine Dreams, Sam Adebayo and Leah Stern sit down with Weimo Liu, CEO and co-founder of PuppyGraph https://puppygraph.com​ Weimo previously worked on query systems at Google and spent time at TigerGraph, and he now helps write the international standard for how these systems get queried.

PuppyGraph’s approach is to check the question before the AI runs it. The product was designed for agents as the user, not humans, and a semantic layer sits over the enterprise data so the AI only needs to know that a teacher teaches a student, not which table holds which or how to join them. The engine handles the deterministic mapping and guarantees accuracy, so the AI reasons and something outside the model verifies. Weimo also covers the Sonnet 4.6 inflection, where early work with customers including AMD became reliable after proper context engineering, how Coinbase runs long-running compliance agents in natural language without writing a single query, and why chaotic enterprise data needs an interactive human-in-the-loop agent rather than automation.

He closes with the warning that stayed with us. One AI agent writes a financial report, another AI agent reviews it, and no human looks at how it was generated, so nobody can know whether the answer is right. He contrasts this with code, where test cases can cover the gaps, and names the human factor plainly: with a tool this powerful, nobody wants to check every detail.

 

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