ITBench: Evaluating AI Agents across Diverse Real-World IT Automation Tasks
International Business Machines · IBM Research · IBM Research - Tokyo, International Business Machines · UIUC · University of Illinois at Urbana-Champaign · IBM Research - Tokyo · University of Illinois, Urbana Champaign · IBM, International Business Machines · , Indian Institute of Technology, Delhi · IBM TJ Watson Research Center · , IBM Research, IBM India Pvt Ltd · UIUC: ECE · T. J. Watson Research Center, IBM
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摘要
Realizing the vision of using AI agents to automate critical IT tasks depends on the ability to measure and understand effectiveness of proposed solutions. We introduce ITBench, a framework that offers a systematic methodology for benchmarking AI agents to address real-world IT automation tasks. Our initial release targets three key areas: Site Reliability Engineering (SRE), Compliance and Security Operations (CISO), and Financial Operations (FinOps). The design enables AI researchers to understand the challenges and opportunities of AI agents for IT automation with push-button workflows and interpretable metrics. IT-Bench includes an initial set of 102 real-world scenarios, which can be easily extended by community contributions. Our results show that agents powered by state-of-the-art models resolve only 11.4% of SRE scenarios, 25.2% of CISO scenarios, and 25.8% of FinOps scenarios (excluding anomaly detection). For FinOps-specific anomaly detection (AD) scenarios, AI agents achieve an F1 score of 0.35. We expect ITBench to be a key enabler of AI-driven IT automation that is correct, safe, and fast. IT-Bench, along with a leaderboard and sample agent implementations, is available at https://github.com/ibm/itbench.