Ben Hylak distinguishes increasing an agent’s best-case capability from raising its reliability floor. He argues that harmful behavior, such as deleting data or sending an unintended customer email, can destroy trust even when the agent performs impressive tasks. Product teams must therefore consider their users’ expertise and how much responsibility the application takes on.
Ben Hylak says evaluation methods inherited from simple chatbots often become brittle as models, tools and harnesses change. He recommends keeping evaluations in code and treating them more like unit and end-to-end tests, because agent behavior depends on the whole environment rather than a prompt string alone. The aim is meaningful safety without delaying improvements for tests that no longer reflect the product.
Ben Hylak recommends tracking when an issue began and how many users it affects before deciding what to fix. Evaluation strategies should also fit deployment scale: a large service can learn from small experiments, while a critical internal application with few users needs a different approach. Small user counts do not make errors unimportant.
Ben Hylak offers three practical lessons for analyzing production traces. Clusters can help exploration but do not necessarily define stable issues or shared root causes. Sandboxed code classifiers can process traces at scale, while deterministic signals such as keyword-frequency spikes give agents specific anomalies to investigate. He advises using agents to explain detected anomalies rather than relying on them to discover anomalies unaided.
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