5Hugging Face Blog·
IBM Research introduces ScarfBench for evaluating AI agents on enterprise Java framework migration
IBM Research published ScarfBench, a benchmark designed to evaluate AI agents on the task of migrating enterprise Java frameworks. The benchmark targets a concrete, high-value software engineering task relevant to large-scale enterprise codebases. It appears on the Hugging Face blog, suggesting an open release or community-facing artifact.
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IBM and UC Berkeley Diagnose Why Enterprise Agents Fail Using IT-Bench and MAST
IBM Research and UC Berkeley have released IT-Bench and MAST, a benchmark suite and diagnostic framework aimed at evaluating why AI agents fail in enterprise IT environments. The work targets realistic IT operations tasks such as incident response, service management, and infrastructure automation. By categorizing failure modes systematically, MAST provides a structured taxonomy for understanding agent shortcomings beyond simple pass/fail metrics. This addresses a gap in enterprise-focused agent evaluation, where general benchmarks often fail to capture domain-specific complexity.
EnterpriseClawBench: A benchmark for enterprise agents derived from real workplace sessions
Researchers introduce EnterpriseClawBench, an enterprise agent benchmark constructed from proprietary real-world workplace sessions, yielding 852 reproducible tasks with fixtures, prompts, role classes, skill subclasses, and semantic rubrics. Because the sessions contain internal enterprise content, the benchmark data is not publicly released, but the construction and evaluation protocol is the reusable contribution. The best evaluated configuration (Codex with GPT-5.5) achieves only 0.663, indicating substantial headroom. The paper argues enterprise agent evaluation must report harness-model combinations, artifact delivery, visual quality, cost, runtime, and skill-transfer behavior rather than collapsing to a single score.
AssetOpsBench: Bridging the Gap Between AI Agent Benchmarks and Industrial Reality
IBM Research introduces AssetOpsBench, a benchmark designed to evaluate AI agents on industrial asset operations tasks, hosted on Hugging Face. The benchmark targets the gap between existing general-purpose agent benchmarks and real-world industrial deployment scenarios. It provides a playground environment for testing agent capabilities in enterprise/industrial contexts.
ITBench-AA: Frontier Models Score Below 50% on the First Benchmark for Agentic Enterprise IT Tasks
IBM Research and Artificial Analysis have released ITBench-AA, a benchmark targeting agentic AI performance on enterprise IT operations tasks. Frontier models evaluated on the benchmark score below 50%, indicating significant capability gaps in real-world IT automation scenarios. The benchmark appears to be the first of its kind focused specifically on agentic enterprise IT workflows, covering tasks relevant to site reliability engineering and IT operations.
UniClawBench: Capability-driven benchmark for proactive agents on real-world tasks
Researchers from HKU MMLab introduce UniClawBench, a benchmark for evaluating proactive AI agents across 400 bilingual real-world tasks organized around five foundational capabilities: Skill Usage, Exploration, Long-Context Reasoning, Multimodal Understanding, and Cross-Platform Coordination. Unlike prior benchmarks, it evaluates agents in live Docker containers with step-by-step checkpoints and a closed-loop multi-turn evaluation strategy using executor, supervisor, and user agents. The benchmark disentangles base model capabilities from agent framework design choices by testing state-of-the-art models across multiple frameworks. Code and benchmark are publicly released.
The Open Agent Leaderboard
IBM Research and Hugging Face have launched the Open Agent Leaderboard, a public benchmark for evaluating AI agents across standardized tasks. The leaderboard aims to provide transparent, reproducible comparisons of open and proprietary agent systems. This initiative addresses the growing need for rigorous evaluation infrastructure as the agent ecosystem matures.
MacAgentBench: New benchmark for AI agents on real-world macOS desktop tasks
MacAgentBench introduces a 676-task benchmark across 25 macOS applications designed to evaluate computer use agents (CUAs) with framework augmentation and fine-grained multi-checkpoint scoring, addressing gaps in existing binary-evaluation benchmarks. Nearly 60% of tasks involve both GUI and CLI interaction, and the benchmark tests 16 models across three agent frameworks. The best result — Claude Opus 4.6 on the OpenClaw framework — achieves 73.7% Pass@1, with performance gains attributed primarily to the skill library rather than framework design. Fine-grained metrics reveal that models with similar Pass@1 scores can differ substantially in sub-goal completion, highlighting limitations of coarse evaluation.
AARRI-Bench evaluates frontier LLMs and agents on granular research-intern-level tasks
Researchers introduce AARR (Act As a Real Researcher), a new benchmark series targeting whether AI agents can emulate the professionalism, thoroughness, and nuanced judgment of human researchers in granular research scenarios—not just macro-level task execution. The first benchmark, AARRI-Bench, tests frontier models and agentic harnesses, finding that even the best configuration (Mini-SWE-Agent with Claude Opus 4.7) achieves only 68.3% success, frequently missing subtle but critical details obvious to human researchers. The work argues that closing the gap requires deeper modeling of research behavior rather than more complex scaffolding.