SWE-bench end-to-end testing shows users if an AI agent succeeds

Evaluating AI agents now demands more than assessing individual function calls; the critical question is whether an agent can reliably complete multi-step tasks in a live environment, even when errors occur. Scoring whether the model sounds right tells you almost nothing about whether the work finished, according to researchers at Berkeley. This shift has led serious agent benchmarks to focus on tool use as the connective tissue for evaluating entire tasks, moving beyond isolated function accuracy to assess actual task completion. A new approach tracks every step of an agent’s process, creating a “trace” to grade both individual actions and the final outcome. SWE-bench utilizes real GitHub issues to verify agent performance. The system assesses whether an agent can not only select the correct functions but also successfully resolve a problem within a live, persistent environment; a trial…