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Published on 27.01.2026
TLDR: Without proper AI evaluations, every new feature you add to your AI app risks breaking existing functionality. AI evals are among the most important skills for building production-ready AI products, especially for non-deterministic agents.
There is a familiar nightmare in AI development. You finish your proof of concept, push it to production, and start shipping new features. As you add functionality, something insidious happens: old features quietly stop working. Suddenly, every code change fills you with dread. Will this break something that was working yesterday?
This is the reality of building AI apps without proper evaluation infrastructure. Unlike traditional software where unit tests provide clear pass/fail signals, AI systems are inherently non-deterministic. The same input can produce different outputs. "Working" is not binary but exists on a spectrum of quality, relevance, and safety.
AI evals serve as the testing framework for this uncertainty. They systematically measure whether your AI system performs as expected across a range of inputs and scenarios. Without them, you are flying blind, discovering regressions only when users complain or, worse, when something goes quietly wrong without anyone noticing.
For teams building AI products, this touches on a fundamental architectural decision. Do you invest in evaluation infrastructure early, when it feels like overhead, or do you bolt it on later when problems become undeniable? The pattern from traditional software development is clear: testing frameworks added as an afterthought never achieve the same coverage or developer trust as those built in from the start.
The challenge is particularly acute with agentic AI systems. When your AI makes sequences of decisions, each step can compound errors. An agent that "usually works" is not production-ready. You need quantifiable confidence in its behavior across the scenarios you care about.
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