Agents' Last Exam
Abstract Overview
Agents’ Last Exam (ALE) is a benchmark for evaluating AI agents on long-horizon, economically valuable real-world tasks with verifiable outcomes. The benchmark was developed with more than 250 industry experts and is organized around 55 subfields in 13 industry clusters, covering non-physical industries grounded in the O*NET / SOC 2018 occupational taxonomy. Tasks are derived from authentic professional workflows, typically requiring combined GUI and CLI interaction, and are evaluated through deterministic or tightly rubriced artifact checks rather than open-ended human judging wherever possible. The paper frames ALE as a living benchmark with a mostly private task pool to reduce contamination and preserve long-term evaluation value.
Novelty
The paper’s main novelty is a benchmark that combines broad industry coverage, authentic expert-sourced professional workflows, and automated verification for long-horizon agent evaluation. Unlike prior benchmarks that are narrower, shorter-horizon, or dependent on human grading, ALE is designed to span all 55 targeted subdomains while preserving realism and verifiability.
Results
ALE is presented at a scale of 1,490 task instances, with non-zero coverage across all 55 subdomains and a public subset of 150 tasks for evaluation. Empirically, current agents perform poorly on the benchmark’s hardest tier where the average full pass rate is 2.6% across mainstream harness and backbone configurations, indicating substantial remaining headroom. The paper also reports that performance varies more by foundation model than by harness among well-engineered systems, while higher runtime or token use does not consistently yield better results.
Key Points
- ALE evaluates generalist computer-use agents on authentic, end-to-end professional workflows that often require both GUI and CLI actions within the same task.
- The benchmark is grounded in a taxonomy derived from O*NET / SOC 2018 and covers 55 subdomains in 13 industry clusters, with tasks contributed and reviewed by domain experts.
- Current benchmark results suggest that economically meaningful long-horizon workflows remain challenging for frontier agents, especially on the hardest task tier.