Discovery and scope
Map the workflow, tools, data, permissions, failure impact and acceptance criteria.
Agent Evaluation Sprint
Turn one important workflow into reproducible evidence.
A focused paid pilot for AI teams that need to know whether an agent can complete a consequential workflow safely—and why it fails when it cannot.
Discuss an evaluation pilotRecommended initial scope
Small enough to start. Complete enough to matter.
One production-relevant agent workflow
Five to ten costly and plausible failure modes
Ten to twenty-five independently verified scenarios
One resettable environment with required tools and state
Oracle, no-action and relevant adversarial controls
Selected baseline models or the customer's current agent
Reviewed trajectories and failure taxonomy
Technical report and next-experiment plan
Delivery stages
A controlled path from workflow to decision.
Evaluation design
Define the failure matrix, representative scenarios, reset behavior and verifier strategy.
Build and attack
Build the environment and run reference, no-op and relevant adversarial controls.
Calibrate and analyze
Run the agreed agent, retain evidence and separate genuine failures from evaluation defects.
Deliver and decide
Demonstrate reproducibility, deliver agreed artifacts and prioritize the next experiment.
Acceptance principles
Agreed before production begins.
Reference passesThe intended behavior succeeds repeatedly.
Wrong behavior failsNo-op and relevant shortcuts cannot earn success.
Every run is traceableResults link to named environment, task and model versions.
Evidence is reproducibleThe customer receives auditable hosted evidence or agreed artifacts.
Start a conversation
Which agent action can you not afford to get wrong?
Share the workflow, current testing gap and impact of failure. We will respond with the smallest useful next step.