Benchspan

{"problem_it_solves": "Benchmarking AI agents is slow (hours to days per run), expensive (hundreds of dollars in tokens), fragile (failures require full reruns), and impossible to collaborate on (no shared source of truth)", "target_customer": "AI agent developers and engineering teams building AI agents who need to evaluate and track performance", "use_cases": ["Evaluating AI agent performance against standard benchmarks", "Comparing experiment results across runs and team members", "Validating agent changes before deployment", "Team-wide benchmark result sharing and collaboration"], "differentiator": "One-time bash-script onboarding vs. complex harness integration; massive parallelization reducing 14-hour runs to minutes; resume-only-failed capability; unified team source of truth with commit-tagged results", "why_now": "AI agent development is accelerating but benchmarking infrastructure hasn't kept pace \u2014 teams are spending days on glue code and waiting hours per run, creating a Research velocity bottleneck", "traction": {"notable_metrics": "28 benchmarks available in library"}}