Language models
Instruction following · Domain reasoning · Factuality
01 / Find the failures
Expert and synthetic evals test reasoning, retrieval, and actions. Production traces reveal where real workflows fail.
Retrieval
Retrieval relevance · Citation quality · Policy scope
Tool-using agents
Tool choice · Permissions · Task completion
Evaluation
Scenario generation · Human review · Regression coverage
02 / Reconstruct in environments
Scroll to explore Keep the inputs behind the failure.
Capture the prompt, retrieved evidence, and tool responses from evaluation and production.
- Task history
- Source evidence
Illustrative scenario
Inspect each decision in context.
Restore captured documents and state to investigate missing evidence, conflicting policies, and wrong decisions.
- Versioned sources
- State snapshots
Illustrative scenario
Let the agent act on realistic tools.
Recreate databases, APIs, and permissions in a resettable environment for the complete workflow.
- Stateful tools
- Multi-step tasks
Illustrative scenario
03 / Run RL loops
Run RL loops with realistic tools, task constraints, and rewards tied to successful completion.
01
Convert failed workflows into training tasks
02
Run rollouts against task-specific rewards
03
Improve reasoning and tool use
04 / Check for regressions
Compare each candidate against reusable tasks and checks, then bring new production failures back into training.
01
Preserve failures as evaluation cases
02
Compare candidates on the same suite
03
Expand coverage as new failures appear