Production Evals For Agentic AI Systems - Nishant Gupta, Meta Superintelligence Labs

Agentic systems require a fundamental shift in evaluation strategy, moving away from static model benchmarks toward production-grade infrastructure. While traditional benchmarks measure isolated model capabilities, they fail to capture the complexities of real-world workflows, including tool failures, API outages, and multi-agent coordination. Reliability must replace raw accuracy as the primary metric, treating evaluation as a continuous operational capability rather than a pre-deployment testing phase. Effective systems utilize a "control plane" architecture that integrates production telemetry, scenario-based simulations, and targeted human review to detect subtle performance drift. By adopting the mindset of a Site Reliability Engineer (SRE), organizations can transition from evaluating simple answers to monitoring complex, autonomous execution traces. Ultimately, production traffic serves as the most representative data source for ensuring dependable outcomes in agentic AI deployments.