Designing Reliable AI Agent Systems Before Scaling Evaluation

Many applied AI teams begin by building datasets and benchmarking models, assuming stronger offline metrics indicate progress. This article argues that for GenAI-based agent systems, such metrics often mask deeper architectural issues that only appear during user acceptance testing (UAT). It highlights common failure points including weak orchestration, misaligned task decomposition, latency perception, and fragile agent boundaries. The authors propose prioritizing method validation with real user scenarios before investing heavily in evaluation pipelines. For technology leaders deploying agentic AI, the lesson is clear: robust orchestration and workflow design must precede optimization and large-scale benchmarking.