Beyond Unit Tests: A Digital-Twin Approach to AI Agent Evaluation

Single-shot evaluation, the shape every current AI eval framework ships, is structurally blind to the failures that take agents down in production: patience that runs out at turn six, users who abandon silently, tool calls that lose conversation state, partial successes that masquerade as wins. This talk argues for a different shape, drawn from the digital twin literature and how the approach is already applied in aerospace, autonomous vehicles, and civil engineering: simulate the population of users your agent will meet, run them against the agent, watch what breaks before any human sees it.

Three take aways:

1. Why current AI agent evaluation cannot detect the failures that hit production. The structural reason prompt-and-grade testing (the shape every current framework ships) is blind to patience, abandonment, conversation state, and partial success.
2. What a realistic synthetic user population looks like, drawn from the digital twin literature in other engineering industries. Four properties: continuous trait clusters instead of fixed persona archetypes; probabilistic dropout (real users walk away silently rather than say goodbye); three-way goal scoring (each conversation is scored “achieved,” “partially achieved,” or “not achieved,” instead of just pass/fail); and variable conversation length (each conversation ends when the persona’s patience runs out, not at a fixed turn count).
3. How to wire population-scale evaluation into a release gate. Why run-over-run delta on a held-constant population is the regression-detection primitive AI agents actually need, and how to treat it as a governance lever rather than a research experiment.”