Turn Production Failures Into Test Datasets | Galileo Dataset Curation

Your agent breaks in production. You fix it. Two months later, the same failure quietly comes back. The input that broke it? Gone. That's not a debugging problem. It's a missing dataset. In this 2-minute demo, see how Galileo turns low-scoring production traces into curated datasets you can test against. Filter a live logstream by Context Adherence score, inspect the trace where the agent hallucinated an S&P 500 price, and capture it to a regression suite with one click. Every failure, every edge case, every response worth protecting lives in the Dataset Store and grows with every debug. Built for teams running AI agents in production who are tired of fixing the same bug twice. What you'll see: - Filtering production traces by Context Adherence score - Diagnosing a tool-output vs. agent-response hallucination - Copying traces into a new or existing dataset - Using the Dataset Store as a regression suite for prompt and model changes 🔗 Try it on your own agent: https://galileo.ai 🔗 Docs: https://docs.galileo.ai 🔗 Sign up free: https://app.galileo.ai/sign-up 🔗 Follow Galileo on LinkedIn: https://www.linkedin.com/company/galileo-ai 0:00 Your agent breaks. The input that broke it is gone. 0:15 That's not a debugging problem. It's a missing dataset. 0:22 Live logstream with metrics on every trace 0:32 Filter for traces below 70% on Context Adherence 0:42 Click into a trace to see what went wrong 0:50 The hallucinated S&P 500 price 1:02 Select traces and Copy to Dataset 1:15 Create a new dataset and name it 1:20 The Dataset Store — every failure worth protecting 1:25 Agents don't stay still. Curated datasets keep up. 1:40 Learn more at Galileo.ai