How to test an AI SDR before launch

An AI SDR launch test should answer a bounded product question. Can this version move the intended buyers from first contact to a valid next step without breaking the rules that protect trust? That question is more useful than asking whether the agent writes good emails, because the product includes targeting, timing, memory, tools, stops, and handoffs.

Write the acceptance criteria before running the test. Include the business outcome, the audience, the workflow horizon, the required evidence, and the failures that block release. A candidate might improve qualified interest while mishandling opt-outs. The gain does not erase the regression.

Keep the production prompts, model, policies, tools, wait logic, and stop behavior in the loop. A simplified demo agent removes the exact interactions that often create failures. The simulation harness should translate between the agent and the controlled world, not replace the agent with a scripted stand-in.

Record the agent version and every relevant configuration. If the result cannot be tied to an immutable release candidate, it cannot support a release review or a later regression investigation.

The test population should include plausible champions, skeptics, gatekeepers, poor-fit accounts, delayed buyers, and explicit stop signals. Let buyer state change after every action. A follow-up that looks harmless in isolation can be wrong after a prior objection or a firm no.

Advance virtual time through delayed replies, scheduled follow-ups, qualification, meeting proposals, calendar actions, and handoffs. Multi-day coverage exposes whether the agent preserves context and acts at the right time, not merely whether it can produce one fluent response.

Use outcomes that can be supported by evidence. A meeting is not booked because the agent says it is booked. The accepted time and successful calendar action both need to exist. Apply the same standard to qualification, stops, and handoffs.

Inspect the cohorts where the candidate version diverges from the baseline. The useful output is not one average score. It is a release record that shows coverage, outcome changes, guardrail failures, representative trajectories, and the harness layer responsible for each important loss.