How to Build Reliable AI Agents with Datasets, Experiments, and Error Analysis

Yujohn from Mastra explains why datasets and experiments are essential for building production-grade AI agents. If you're building an agent, you need a way to verify it's working correctly before and after you make changes. Datasets provide that baseline. You create a collection of test cases (ground truth) that represent the scenarios your agent should handle. Then you run experiments: pass each test case through your agent and measure the results. This is error analysis in practice. You start by identifying where your agent fails, then build scorers to quantify those failure modes over time. Smaller teams often ship first and add datasets later, once they have user feedback. Larger teams need them earlier. But eventually, every production agent needs this. The demo shows how Mastra makes this accessible. You can create datasets through the UI, add items manually or import from CSV, and run experiments with a single click. The results show you exactly what went wrong: which tool calls failed, what the agent output was, and how it compared to ground truth. You can also compare experiments side by side to see if your prompt tweaks actually improved things. And because all the data lives in your own database, you can write your own agents to analyze the results, dig into traces, and iterate. The SDK makes it easy to integrate into CI/CD: run experiments on pull requests, gate deployments on eval scores, or just collect data from production and curate datasets later.