A new arXiv preprint introduces EGI-BENCH, a benchmark suite for enterprise general intelligence. The authors argue that enterprise adoption of LLMs in agentic workflows is gated by evaluations that must cover a wide range of abilities, not just single-turn question answering or basic retrieval.
The benchmark spans several areas central to enterprise work: CRM operations, heterogeneous-document retrieval, personal data understanding, multi-turn planning, tool use, and function-related tasks. By grouping these capabilities into one suite, EGI-BENCH aims to provide a more holistic picture of how well a model can handle real-world enterprise scenarios.
Because the abstract is the only information available, the exact structure of the benchmark and any preliminary results are not yet known. Still, the framing suggests a move toward evaluation suites that reflect the complexity of agentic systems, where models must combine retrieval, planning, and tool interaction over multiple turns.