Mallika Rao

As organizations race to build AI agents, many teams are encountering challenges that feel new: evaluation uncertainty, feedback loops, behavioral drift, exploration versus exploitation, and maintaining user trust in systems that continuously adapt.But these challenges are not new.

Modern personalization systems are shifting from hand-tuned heuristics to AI-native architectures, but building an adaptive recommendation engine in production—one that continuously learns, evolves, and delivers measurable business value—requires far more than deploying a model.

As organizations adopt AI at scale, evaluation becomes the backbone of trust, safety, and product readiness. Yet building effective evals is deceptively hard: there is no single metric or benchmark that captures the complexity of user-facing AI systems.