Agent Drift - Understanding and Managing AI Agent Performance Degradation in Production

Learn to identify, measure, and mitigate Agent Drift—the gradual performance degradation that occurs when AI agents encounter evolving data, shifting user behavior, changing tools, or updated model parameters in production environments. Discover the root causes behind agent drift and understand how it impacts reliability, accuracy, and overall system trust in this 28-minute conference talk by Kumaran Ponnambalam, Principal AI Engineer at Cisco, recorded live at the MLOps World GenAI Summit 2025. Explore practical frameworks for measuring and tracking AI agent performance, including proactive observability techniques and performance monitoring strategies. Master detection methods for early signs of drift across data patterns, behavioral changes, and model interactions. Implement effective mitigation strategies through adaptive retraining approaches, calibration techniques, and dynamic logic adjustments. Build resilient, self-correcting agent systems capable of maintaining high performance at scale in dynamic, real-world production environments, ensuring consistent long-term reliability for AI agents that serve as core components of production systems.