As organizations scale AI adoption, traditional monitoring tools are struggling to keep up.

Long-lived connections, elevated error rates, and complex real-time pipelines require observability built for how AI systems actually behave.

But many teams struggle to pinpoint why LLM infrastructure breaks in ways traditional monitoring cannot detect, and how to separate real incidents from expected AI error behavior.

As a result, scaling efficiently has become increasingly challenging.

In this exclusive live session with Datadog, four leading experts will explore how unified observability helps teams detect issues earlier, resolve them faster, and turn production context into intelligent action.

This session will focus on what teams can do now to stay ahead.

What you’ll learn:

  • How leading teams monitor LLM systems in real-world production environments
  • How to identify and resolve issues before they impact users
  • How to bring observability directly into AI workflows and decision loops
  • How to reduce MTTR and improve reliability across AI-driven systems

Speakers

Andy Keogh
Sales Engineer, Datadog

Andy Keogh serves as a Customer Success Sales Engineer at Datadog, where he works with customer success teams and existing customers to showcase Datadog’s value, support onboarding, and align observability initiatives with key business outcomes.

John Trapani
Field CTO, Datadog

John Trapani serves as Datadog’s Field CTO for Financial Services, where he partners with leaders in banking, capital markets, and insurance to align observability strategies with their most important business outcomes.

Nicolas Chinot 
GM US, Dust

Nicolas Chinot is the US General Manager at Dust. He was one of its first investors and later founded Dust’s US business, which he now leads. Nicolas previously spent several years as a Product Lead at Square, which he joined through the acquisition of a startup he founded during college.

Jean-David Fiquet
Software Engineer, Dust

Jean-David Fiquet is a Software Engineer at Dust, where he helps build an AI operating system for forward-thinking companies. Jean-David works on the core platform that enables organizations to deploy secure, context-aware AI agents connected to company knowledge and tools.