Keeping Models Honest: AI Observability for Scalable, Reliable Systems

About this session

As AI systems grow more complex—spanning multimodal models, pipelines, and autonomous agents—the need for observability has never been greater. This panel brings together ML engineers, data scientists, and infrastructure leaders who are building the next generation of AI monitoring and debugging tools. Panelists will explore best practices for tracking model performance in production, detecting drift and bias, tracing decision flows, and correlating signals across distributed systems. Expect candid insights into how observability differs from traditional monitoring, what it takes to achieve real-time visibility into LLMs and agentic systems, and how teams are designing feedback loops that keep models adaptive, transparent, and aligned with business outcomes.