Many AI agent demos look impressive but fail under real-world conditions. This article argues that developers should run their own task-specific evaluations rather than relying on vendor benchmarks. It outlines key metrics like task completion rate, error recovery, and latency under load. For teams building agent-based systems, this is a wake-up call to prioritize empirical testing over marketing claims. The post also suggests open-source tools for creating custom evaluation suites, making it actionable for engineering leads.
A critical look at AI agent benchmarks reveals marketing often outpaces reality. Learn a practical framework for evaluating agent performance in real tasks.