How AI Engineers Actually Use Datasets: Test Cases, Edge Cases

Most AI agent discussions focus on models. In practice, the model is rarely the problem. When you build an agent today you are almost certainly not training it. The model is fixed. What determines whether the agent actually works is everything around it: the tools it can call, the prompts that guide it, the logic that decides what it does next. So when people say "we need more data," they usually do not mean training. They mean better test cases, clearer failure scenarios, and a way to measure whether the agent is behaving correctly. This article breaks down how to evaluate an AI agent properly: what to test, how to structure realistic scenarios from real world data, how to score the path the agent takes not just the answer it lands on, and how to design adversarial tests that force actual reasoning instead of pattern matching. Using SRE agents as the concrete example throughout. What You Are Not Doing vs What You Are Before anything else, this distinction matters. What you are NOT doi