Agent Bench Transparency

Expose conflicts of interest in agent benchmarks like credit ratings

Problem

AI agent companies buy benchmarks to prove reliability to customers, creating a credit-rating-style conflict where the rated party pays. Developers struggle to judge benchmark credibility, lacking a basis for purchasing decisions.

Solution

An independent platform that collects and analyzes benchmark funders, methods, and results, providing transparency scores and conflict warnings. Core: benchmark database, conflict detection, user reviews.

Users

Enterprise tech decision-makers purchasing AI agents, AI researchers

MVP

Manually enter common agent benchmarks (e.g., SWE-bench) funders and methods; simple conflict score; public leaderboard and reviews; allow submissions

Revenue

Free public data; paid enterprise reports and API access

Why now

Agent market is exploding, benchmark credibility issues are surfacing, and historical credit-rating lessons resonate; indie developers can build trust through neutrality