The Missing Layer in AI Agents: Evaluation, Tracing, and Failure Detection Before Production Breaks
Most AI agent articles stop when the demo works.
The model answers a prompt, maybe calls a tool, returns something sensible, and the article calls that progress. But the hard part of AI systems is not getting a first response. The hard part is knowing whether the system keeps behaving correctly after the fifth prompt, the tenth tool call, the bad user input, the partial failure, the model update, and the unexpected edge case that nobody thought to test.
That is the layer this article is about.
If you are building AI agents that actually ship, you need more than prompts and tools. You need an evaluation and observability system that tells you when the agent is drifting, where it is failing, what it is doing before failure, and whether a release made the system better or worse.
In other words, you need a way to answer a deeper set...
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