Agent evals should feel like real work (www.zohaib.cc)

Evaluating AI agents requires a shift from focusing solely on their final outputs to assessing their overall functionality within realistic work scenarios, according to insights shared by Cameron Wolfe. Unlike traditional tasks for language models, which may simply check a final answer, effective evaluation of AI agents should consider factors such as planning, tool usage, error recovery, and real-world outcomes. For instance, if a coding agent claims to have fixed a bug, the true measure of success lies in whether the relevant tests pass, rather than just the assertion of completion. This new perspective emphasizes the importance of designing evaluations that simulate real tasks, encompassing a defined environment, operational tools, and clear success criteria. By preserving the trace of each agent's actions—including tools employed, paths taken, and errors encountered—evaluators can better analyze agent performance and debug issues effectively. While traditional subjective assessments still have value, the article advocates for prioritizing reproducible, code-checkable methods in evaluations to ensure that AI agents reliably complete their intended jobs, ultimately leading to their successful integration into real-world applications.