EGI-BENCH: An Enterprise General Intelligence Benchmark Suite
We release EGI-BENCH, a unified LLM agent benchmark suite that consolidates eight enterprise facing agent benchmarks (CRMARENA, HERB, PERSONABENCH, USERBENCH, MCP-UNIVERSE, MCPEVAL, BENCHFORCE, LOCOBENCH) behind a single command-line interface with two runtime-budgeted execution modes.
ProofPaper ↗
Key points
- Enterprise adoption of LLMs in agentic workflows is gated by evaluations that span a wide arc of abilities: CRM operations, heterogeneous-document retrieval, personal data understanding, multi-turn planning, tool use, function calling, long-horizon research, and long-context software engineering.
- These evaluations live today in separate repositories with divergent runners, dependencies, and scoring conventions, which makes cross-benchmark comparison difficult and obscures what a single aggregate number actually represents.
- On top of the suite we define the Enterprise General Intelligence (EGI) Score, a six-dimensional framework (M1-M6) whose buckets are populated by fine-grained sub-metrics routed from each benchmark rather than by benchmark-level averages.
- We describe the suite, the sub-metric routing, and the quick/deep budgets, and report results for fourteen frontier and cost-efficient LLMs spanning the GPT, Claude, and Gemini families under quick mode.
Sources (1)
- [1]EGI-BENCH: An Enterprise General Intelligence Benchmark SuitearXiv (AI, ML, NLP, CV, robotics, multi-agent) · Oct 19, 08:02 PM
We release EGI-BENCH, a unified LLM agent benchmark suite that consolidates eight enterprise facing agent benchmarks (CRMARENA, HERB, PERSONABENCH, USERBENCH, MCP-UNIVERSE, MCPEVAL, BENCHFORCE, LOCOBENCH) behind a single command-line interface with two runtime-budgeted execution modes.
Enterprise adoption of LLMs in agentic workflows is gated by evaluations that span a wide arc of abilities: CRM operations, heterogeneous-document retrieval, personal data understanding, multi-turn planning, tool use, function calling, long-horizon research, and long-context software engineering.
Extractive summary: sentences quoted from the sources.
Before this
- Oct 19, 2026Same Simulation, Different Evidence: Where Retrieved Tool Output Costs LLM Agents Accuracy
- Oct 19, 2026Introducing Clairic, a new System One class of extraction models
- Oct 19, 2026The Model Swap Workshop: GPT-5.5 vs Kimi vs DeepSeek vs Mistral
- Oct 19, 2026Agent World Model in the Wild: Computer-Use Agent Environment Synthesis
- Oct 18, 2026Google Open-Sources Harness2, a Recursive Agent Harnessing Framework
- Oct 17, 2026Blown away by Minimax H3 sometimes. It nailed this one!!!!