Operations | Monitoring | ITSM | DevOps | Cloud

AI coding tools are accelerating development velocity, creating a release challenge most teams aren’t equipped for. Without controlled rollout, higher change velocity makes it harder to know which specific release drove the results you’re seeing in production. And when teams use AI, to build AI – LLM apps and AI agents– complexity multiplies. Traditional observability can’t ensure AI agent quality, performance, and cost-efficiency at production scale.

AI coding assistants are rapidly evolving from passive copilots into active, agentic collaborators capable of planning, executing, and iterating on complex software tasks. This shift has huge ramifications onthe software development lifecycle (SDLC), developer productivity, and even the structure of engineering teams.

The way we build, ship, and run software is being reshaped by AI. In this fireside chat, Yanbing Li (CPO, Datadog) and Tom Occhino (CPO, Vercel) will discuss their perspectives on the impact AI is having across the industry and what it means for teams navigating this shift today.

AI’s ability to write code made huge strides over the past year. Today, coding agents aren’t just assisting developers; they are winning the "coding race" by orders of magnitude and fundamentally changing the way engineers work.

The breakthroughs in AI today aren’t just coming from bigger datasets and more compute; Reinforcement Learning (RL) has quietly become one of the most powerful forces in modern AI development. RL is teaching models to reason and self-correct, enabling capabilities that make AGI feel less like science fiction and more like an inevitable future.

Bad data doesn't announce itself. Datadog Data Observability gives you unified visibility across your entire data stack—from source systems and pipelines to dashboards and AI applications—so you catch silent failures before they cascade. Detect data quality and pipeline issues before stakeholders do, pinpoint root causes with end-to-end lineage, and reduce pipeline costs with job, cluster, and query recommendations.

At, Datadog launched 100+ capabilities to help customers drive autonomy and manage growing AI and security complexity. From new Bits AI, log management, and security capabilities, customers have the visibility and autonomous operations they need to detect, investigate and resolve issues across the development loop and data lifecycle. Tune in to the full keynote to catch the highlights.

In this video, you'll learn how Datadog GPU Monitoring gives ML and platform teams a single view of their GPU fleet, so they can see what's slowing down their AI workloads, fix issues faster, and use the GPUs they already have more efficiently.

"We're doing cutting-edge AI, focused on real translational impact: getting our research over the wall and into production." Ameet Talwalkar, Datadog's Chief Scientist, shares what it took to build the AI Research Lab from the ground up — and what makes DAIR different from traditional research teams. At Datadog, research ships. Recent work from the lab includes Toto 2.0, open-weights time series forecasting models ranked on leading benchmarks, and ARFBench, a new benchmark for evaluating AI on real incident data.

Datadog has always been driven by a broader vision of helping teams understand and operate complex systems. In this session, you’ll hear from Michael Whetten, Product SVP, and Abrar Hussain, Senior Director, Product Management, as they share the latest updates across the Datadog product suite and discuss how that vision continues to shape the platform’s evolution and support the next generation of AI-driven applications.