Turn AI SDR production failures into regression tests

Start with the complete production trajectory and its supported outcome. Determine what the buyer state was, what the agent observed, which actions and tools followed, and where the behavior first became unacceptable. A metric anomaly without this review is not yet a regression scenario.

Record the agent version and operational context. This prevents a later team from testing the wrong failure or attributing it to a component that was never involved.

Remove unnecessary personal and customer data before creating the scenario. Keep only the state, sequence, constraint, and tool conditions required to reproduce the behavior. Document what was transformed and require human review before the scenario enters a shared suite.

The aim is not to recreate a specific person. It is to preserve the general failure pattern, such as losing an objection after a long delay or sending after a stop signal was recorded in another system.

A completed world should remain stable so historical comparisons stay meaningful. Add the new scenario through a reviewed world version or regression suite rather than mutating the artifact used for an earlier release.

Specify the expected state transition, acceptable behavior, blocking failure, and evidence required to pass. Leave room for the agent to solve the situation through more than one valid trajectory.

Run the candidate against the incident-derived scenario and the broader frozen buyer population. A narrow fix can solve one example while weakening other cohorts, changing qualification behavior, or creating unnecessary handoffs.

After release, monitor the new version and retain the relationship between the original incident, regression scenario, candidate comparison, and live result. That closed record shows whether the system learned from production rather than merely closing a ticket.