Simulation Techniques for AI Agents from Self-Driving

Join the MLOps Community https://mlops.community/join As conversational AI systems become increasingly autonomous and mission-critical, ensuring their reliability presents novel challenges that parallel those faced in self-driving vehicle development. This talk explores how simulation-based testing and evaluation strategies from autonomous vehicles can be adapted to build more robust AI agents. We'll examine how traditional software engineering practices are evolving in response to AI systems, where deterministic unit tests give way to probabilistic performance metrics and behavioral analysis. Drawing from real-world examples, we'll demonstrate how comprehensive telemetry — both in pre-production simulation and production environments — provides crucial insights beyond simple pass/fail metrics. The presentation will delve into the critical balance between computational cost, latency requirements, and signal quality in AI system evaluation. We'll introduce a framework for developing evaluation strategies based on reliability requirements across different use cases, from bug detection tools where any true positive provides value, to medical assistance systems that demand near-perfect accuracy. Attendees will learn practical approaches to implementing simulation-based testing pipelines, techniques for meaningful telemetry collection and analysis, and strategies for defining appropriate reliability thresholds based on application context. This session will benefit ML engineers, software architects, and engineering leaders working on production AI systems. //Bio Brooke Hopkins is the founder of Coval, a simulation and evaluation platform for AI agents. She previously led evaluation job infrastructure at Waymo. There, her team was responsible for the developer tools for launching and running simulations, and she engineered many of the core simulation systems from the ground up. A Prosus | MLOps Community Production