Engineering Reliable Agentic AI through Systematic LLM Evaluations

Building reliable agentic AI applications requires *systematic evaluations* to navigate the inherent unpredictability of *large language models**. Developers must implement a three-tiered evaluation framework comprising **unit tests**, **human and model reviews**, and **A/B testing**. By utilizing an **analyze-measure-improve lifecycle**, engineers can closely inspect real data and **failure modes* to address performance gaps. Rather than relying solely on *off-the-shelf tools**, teams should begin with basic **binary metrics* and *manual inspections* before scaling to automated solutions. Ultimately, combining rigorous *code assertions* with *human-aligned LLM judges* enables continuous, dependable system improvements.