Enterprise-Bench Methodology
The full methodology paper.
Enterprise-Bench Methodology
A research-grade deep dive into how we built the first open benchmark for enterprise AI — the evaluation framework, dataset construction, scoring rubrics, and statistical methodology behind Enterprise-Bench 1.0.
Written for analysts, researchers, and technical evaluators. 42 pages.
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Why we published this.
Every major technology category eventually gets a neutral measurement standard. Databases got TPC-A in 1985. Networking got the RFC process. Machine learning got SQUAD, GLUE, and ImageNet. Before those standards existed, buyers chose between competing vendor claims and gut feeling.
Enterprise AI agents are at that exact inflection point today. Every vendor — Agentforce, Agentspace, Sierra, Glean, Decagon — touts capability. None can be compared on a neutral basis. The market is choosing AI systems that touch live customer data, close real deals, and route real support tickets based on demos and trust.
We built Enterprise-Bench to close that gap. Not as a vendor marketing exercise — as an open, reproducible standard that any agent can be measured against. The task suite is public. The dataset is versioned and published. Competitors run the benchmark against their own instance and submit results by pull request. We never claim to benchmark anyone ourselves. That is the credibility unlock.
This paper is the full technical specification behind that benchmark. It goes deeper than the public thought piece — into the evaluation framework, the dataset construction methodology, the statistical design, and the reproducibility guarantees that make Enterprise-Bench worth citing in a Gartner evaluation or a procurement scorecard.
If you're evaluating AI agents for your organization, this is the document that tells you exactly what was measured, how, and why those measurements matter.
What's inside
Four chapters covering the complete Enterprise-Bench specification — from task taxonomy to statistical validation. Each section is designed to stand alone for reference.
L1-L4 task taxonomy
The full autonomy-level framework with scoring rubrics and worked examples. What distinguishes retrieval (L1) from multi-hop reasoning (L2), and what L3/L4 look like as the benchmark evolves.
Dataset construction
How we built a synthetic B2B enterprise — 42 accounts, 5 systems of record, realistic cross-system relationships — and scaled it 256x while maintaining ground-truth integrity at every level.
Evaluation framework
LLM-judge design, the three scoring axes (precision, efficiency, safety), verifier architecture, and the reproducibility guarantees that make results citable in analyst reports and procurement.
Statistical methodology
Trial design, variance handling across runs, significance thresholds, and how we ensure benchmark results are stable enough to compare across vendors and time periods.
Get the full methodology paper.
42 pages. Research-grade. The complete specification behind Enterprise-Bench 1.0.
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