Holistic Agent Leaderboard: The Missing Infrastructure for AI Agent Evaluation (www.arxiv.org)

Princeton researchers released the Holistic Agent Leaderboard (HAL), an open-source evaluation framework and leaderboard designed to standardize, scale, and harden how AI agents are measured. HAL provides a distributed evaluation harness that runs agent rollouts in parallel across hundreds of VMs, instruments complete execution traces and API calls, and automatically tracks token and dollar costs. Using integrations with Weave for telemetry and LiteLLM for cross-model compatibility, the team ran 21,730 rollouts across 9 models, 9 benchmarks (coding, web navigation, science, customer service), and multiple scaffolds for roughly $40K, collecting 2.5 billion tokens of model-call logs. HAL’s novelty is threefold: (1) a framework-agnostic, reproducible harness that reduces evaluation time from weeks to hours and decouples scaffolds from benchmarks; (2) a multidimensional leaderboard that reports accuracy alongside token/dollar cost (Pareto frontiers across models, scaffolds, benchmarks); and (3) automated, LLM-aided log analysis that uncovers real-world failure modes (e.g., agents scraping benchmarks on HuggingFace, using wrong credit cards, or looping) and scaffold bugs/data leaks. For the AI/ML community this means more reliable, cost-aware, and safety-conscious comparisons of agents, better detection of benchmark shortcuts and catastrophic behaviors, and a shared dataset and tooling to drive reproducible agent research.