ENTERPRISE-BENCH METHODOLOGY
Read the full methodology
There was no open, vendor-neutral benchmark for enterprise AI – so we built it. Learn how we did it, what it revealed, and why it matters for our industry.
Written for analysts, researchers, and technical evaluators.
Full L1-L4 taxonomy with worked examples
Dataset construction and 256x scaling approach
Evaluation framework and reproducibility guarantees
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Why we published this
Every major technology category eventually gets a neutral measurement standard. Databases got TPC in the early 1990s. Networking got the IETF BMWG framework. Machine learning got ImageNet, then SQuAD, then GLUE. Before those standards existed, buyers had to choose based on competing vendor claims and best guesses.
Enterprise AI has now reached that vital moment. Every vendor is shouting about their own capability. None can be compared on a neutral basis. Buyers have to choose an AI system – that touches live customer data, closes real deals, routes real support tickets – based on limited demos and vague promises.
We built Enterprise-Bench to close that gap. Because our industry needs an open, reproducible standard that any agent can be measured against. The task suite is public. The dataset is versioned and published on Harbor Hub. The evaluation harness – built by Laude Institute, and independently validated by Professor Alexandros Dimakis (UC Berkeley) – is available for independent reproduction. Any vendor or organization can run the benchmark, then submit results to the public leaderboard.
This paper is the full technical specification behind that benchmark. It covers evaluation framework, dataset construction, statistical design, and reproducibility guarantees – with the rigour depth and rigour a Gartner evaluation or procurement scorecard demands.
Evaluating AI agents? Point your shortlisted vendors at this benchmark, and ask them to run it. The methodology is open. The dataset is public. The only question is: who’s brave enough to be benchmarked?
What's inside
Nine sections plus appendices covering the complete Enterprise-Bench specification - from autonomy framework to market implications. Each section is designed to stand alone for reference.
L1-L4 autonomy framework
Four graduated levels of agent capability, from reactive (L1) to autonomous (L4), with scoring rubrics, the "wide L1" innovation, and worked examples showing what passes and what fails.
Dataset construction
A mid-market B2B payments platform modeled with 42 accounts across 5 interconnected enterprise systems, then scaled to 256x with answer-preserving data scaling.
Multi-tier tool interfaces
The controlled experiment isolating interface complexity from reasoning capability. Curated vs. protocol-realistic API surfaces, and the 18-19 point accuracy gap between them.
Evaluation and statistical design
Independent LLM judge. Three scoring axes (precision, efficiency, safety). Pass@k methodology – because "works once" isn't "works reliably." Controlled design. Fully reproducible.
Get the full methodology paper
The new benchmark standard is here. Read the complete specification behind Enterprise-Bench.
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