DevRev, maker of the agentic AI product Computer, announced Enterprise-Bench, an open, vendor-neutral benchmark for evaluating whether AI agents can operate in production enterprise environments. The benchmark measures conditions including fragmented data, siloed systems, and permission boundaries, and was developed with Laude Institute, whose Harbor harness executed the evaluation, and validated by Alexandros Dimakis, UC Berkeley professor. On identical tasks with the same underlying model, Computer, by DevRev was 48% more accurate and 4.4x more token-efficient than Claude. According to McKinsey's 2025 State of AI survey, nearly two-thirds of organizations have not yet begun scaling AI across the enterprise and in any given business function, no more than 10% are scaling AI agents. The framework, dataset, methodology, results, and full traces are public. Enterprise-Bench's L1-L2 benchmark, spanning factual retrieval and complex multi-source queries, is the first release, with L3 and L4 planned for later this year and into next.