Service
Agent Evaluation & RL Environments
Evaluation suites and reinforcement-learning environments that show whether an AI agent really does the job, before your customers find out.
Who it’s for
AI teams shipping agents into production, and companies building environments and benchmark tasks for model training.
What we do
- Task suites and graders that measure an agent against your real workflows
- Reinforcement-learning (RL) environments with reproducible, sandboxed tasks
- Terminal-Bench-style tasks for coding and operations agents
- Reward-hacking and grader-robustness reviews
- Regression evals in CI, so every model or prompt change is measured
- Domain-specific evals for regulated work such as KYC, lending documents and claims
How we approach it
An evaluation is only as good as its tasks. We design tasks from real work, write graders that check outcomes rather than surface text, and try to break them before an agent does. You end up with a suite you own and can run on every change.
Relevant experience
- Delivered RL environment and agent-training projects for an AI company that works directly with frontier labs
- Expert consultant and domain advisor on frontier-model training work, including Terminal-Bench-style tasks
Work led by our principal architect. Client names are withheld under confidentiality agreements.
Technologies
Next step
Tell us what you’re working on
Send a short description of your project or problem. You’ll get a reply from our principal architect within two business days, with an honest view on whether we’re the right fit.