Private evaluations, custom scenarios, and deployment diagnostics for teams building production AI agents. Find the failure modes your users will discover before they do.
Enterprise evaluation packages include private infrastructure, custom scenario design, and ongoing support for continuous agent improvement.
Run evaluations on your proprietary agents in isolated infrastructure. Your agent code, prompts, and results never touch shared systems or appear in public benchmarks.
Scenarios designed around your specific use case. Whether you're building customer service agents, enterprise assistants, or specialized domain experts, we build evaluation environments that match your deployment context.
Detailed failure mode analysis that tells you exactly what's breaking and why. Not just scores, but actionable diagnostics with annotated transcripts showing where your agent went wrong.
Continuous evaluation as you iterate on your agent. Track performance across versions, catch regressions before they hit production, and build confidence that improvements are real.
From initial conversation to ongoing evaluation in four steps.
We learn about your agent, your use case, and the failure modes you're most concerned about.
We build evaluation scenarios tailored to your deployment context and success criteria.
Initial run with detailed diagnostics. You see exactly how your agent performs and where it breaks.
Continuous evaluation as you iterate. Catch regressions and validate improvements.
Compare model variants, post-training changes, and behavioral regressions. Surface the differences that matter in persistent multi-turn interactions where standard benchmarks show no signal.
Surface persistence, deception, and social adaptation failures that static evals miss. Test how your agent handles adversarial social pressure, conflicting objectives, and long-horizon commitments.
Test custom scenarios before launch and track regressions over time. Know exactly where your agent breaks in the conditions that match your production environment.
Start with a pilot evaluation. See exactly where your agent breaks and get actionable recommendations for improvement.
Questions? contact@cruciblebench.ai