The hype around agentic AI often rests on flashy demos and impressive benchmark numbers, but these metrics rarely translate to dependable performance in production. This analysis examines why current evaluation methods fall short, from cherry-picked scenarios to overfitting on narrow tasks. It advocates for a shift toward task-oriented testing that measures robustness, error recovery, and long-horizon planning. For engineering teams, this means building custom evaluation suites that mirror actual use cases, rather than relying on generic leaderboards. The piece also touches on the economic implications: unreliable agents can erode trust and increase operational costs. As agentic systems move from research to deployment, the community needs shared standards for what 'good' looks like. This signal is a call to action for developers to prioritize evaluation design as much as model architecture.
A critical look at how agentic AI is evaluated, arguing that demos and benchmarks fail to capture real-world reliability and proposing a more practical framework.