Train your own agenton your
expertise,one run at a time.
Turn your company’s expertise into better agentsInspect sessions and train on datasets prepared in your own workflow.
Your expertise is your moat
A foundation model is a starting point. Your internal knowledge, ways of working, and standards of quality give your agents something distinctive to learn.
Your sessions
The decisions, tool calls, and corrections that show how your team works.
Your tasks
Real problems from your business give agents something meaningful to learn from.
Your standards
Processes, policies, and accepted examples define what good looks like. Verifiers make it testable.
Your expert feedback
Approvals and corrections capture the judgment behind great work for your team to review.
One proxy in front of your agents.
Connect supported tools through Gateway and use captured trajectories to inspect and compare agent behavior.
Connect supported agents through the proxy
A loop that compounds.
Workflow illustration — a conceptual training loop.
- Ingest data
- Evaluate task
- Choose a model
- Curate a dataset
- Fine-tune weights
- 01
Ingest data
Collect interactions, tool calls, and corrections as rollouts for a training dataset.
- 02
Evaluate task
Score rollouts against task-specific checks and review the evidence before selecting examples.
- 03
Choose a model
Choose a model for the task and compare its results with the same evaluation.
- 04
Curate a dataset
Organize selected traces into a dataset for review and reuse.
- 05
Fine-tune weights
Use selected data in a training run, then evaluate the resulting model and repeat.
Find the task. Follow the evidence.
Understand the task, follow the trace, and compare the outcome.
Read the task, inspect its environment, and see what the verifier rewards.
Go beyond the score. Follow an agent's tool calls and observations in your trajectories.
Explore published leaderboards to compare reported results on the same task.
Track your recent trials, statuses, and results.
From a task to your next experiment
Find the tasks, data, and results that move your work forward.
Workflow illustrations — sample scenes, not live activity or measured results.
Trajectories
Inspect an agent’s tool calls, responses, and outcomes, step by step.Tasks
Explore tasks with clear instructions, environments, and verifiable outcomes.Built for research
Better experiments start here.
Tasks, traces, and datasets for evaluation and post-training.