Turn representative work, failure evidence and operating constraints into a reviewable decision—not a leaderboard screenshot.
Complete the stages in order. Capability scores cannot compensate for prohibited behavior or failed security, privacy, legal and operational gates.
Name the configured system, users, workload, acceptance boundary, prohibited outcomes and evidence required for ship, bounded trial or reject.
Precommit the decision rule →Sample representative tasks and environment failures. Version prompts, tools, permissions, graders, time limits, reviewer rules and stop conditions before running candidates.
Build the evaluation protocol →Preserve trajectories, interventions, unsafe attempts, latency and cost per accepted outcome. Refuse a ranking when the evidence cannot separate candidates.
Carry evidence into procurement →This sequence is AccessAllGPT editorial guidance, not an original benchmark, an empirically validated scoring system or a vendor ranking. The linked guides identify their sources, methods and limitations.
A private evaluation improves local relevance but does not establish universal performance or eliminate rare failures. Teams must test the exact configured system, disclose changes and adapt controls to their workload and consequence level.
Read publication disclosures →Methods for preserving case-level evidence, consequential failures, human intervention and uncertainty.
A reproducible retrieval test and deployment gate for choosing overlap from evidence rather than a framework default
In a deterministic boundary test, zero overlap never kept the two required facts together. Twenty-five percent fixed every case; raising overlap to 75% added no retrieval win and produced three times as many chunks.
Measure systems, interventions and failure—not demos
A benchmark design for teams evaluating tool-using agents on their own operational tasks.