Join Datadog CPO Yanbing Li and a special guest as they discuss emerging technologies and innovation, how they impact businesses today, and the new opportunities they create for you.
This guest blog post is by Tohn Furutani, SRE Engineer at NTT DATA. Over the past year, the conversation around generative AI has shifted from single-shot use cases—such as summarization, Q&A, and chat interfaces—to agentic AI systems that can make decisions based on context, plan multistep actions, invoke tools, and adapt as conditions change.
You’re told to “go build agents” without clear guidance on what that actually means, how to do it well, or how to know if it is working. You are not a data scientist. You are a software engineer. In this talk, a Datadog AI product leader Shri Subramanian breaks down what changes when you move from building applications to building AI agents, and why familiar approaches like traditional testing and linear delivery fall short. We will explore how agent development shifts the focus from code alone to data, prompts, and evaluation, and why functional reliability matters just as much as operational reliability.
Delivering great products to your customers requires a mix of evolution and consistency. To really land with users your product has to be ready to adapt and scale, prioritizing across a mix of customer and business needs. Join experts in reliability, systems engineering, and DevOps as they share real-world examples, true stories of pitfalls, and astounding impact from the experiments they have run. Learn how experienced practitioners handle failure, adapt to scale, and bridge gaps between teams to improve software performance and customer outcomes.
When stakeholders push for faster growth (new markets, new features, newly modernized stack) your engineering model has to change too. At FitnessPassport, the shift from offshore waterfall delivery to an in-house team meant rebuilding not just services, but confidence: legacy systems with weak logging and little visibility made it hard to know whether changes were working and impossible to spot issues before users did. In this talk, Director of Engineering Rob Mitchell will share how FitnessPassport adopted Datadog and used structured logs, metrics, and traces to tighten feedback loops.
Platform teams often end up as the bottleneck for “small” operational asks: add a new button, wire up a workflow, expose one more cloud capability—each change requiring engineering time, reviews, and releases. In this technical deep dive, engineers from the Department of Government Services (Victoria) share the architecture and open source CDK library behind their “Infrastructure Control Panel”: a modular operational enablement app that lets non-technical users interact safely with cloud resources through strong access controls.
Datadog Session Replay heatmaps track where users click, scroll, and engage across your web pages. Each heatmap is overlaid on a screenshot of the page, and that background determines what you can actually analyze. But getting the right screenshot can be tricky. Many UI states are dynamic, rare, or simply impossible to capture from replays, so heatmaps can end up showing the wrong view.
ClickHouse is widely used for large-scale analytics, but once it is running in production, it can be difficult to understand how query activity translates into resource usage. Engineers investigating performance issues often struggle to determine which queries consume the most memory, run most frequently, or cause spikes in load. In practice, engineers are left querying system.query_log, tailing server logs, and piecing together information after an incident.
Being on call is an essential part of operating reliable distributed systems, but it comes with real human costs such as alert fatigue, sudden wakeups in the middle of the night, and the ongoing anxiety of what the next notification might bring. Many engineers know the feeling: Your phone lights up, a sound cuts through the silence, and your heart rate spikes before you’re even fully awake.
Finding the right information across dashboards, monitors, and telemetry sources takes time, even for experienced engineers. When something breaks, it often means figuring out where to start, rebuilding queries, and jumping between metrics, logs, and traces before you can take action. The challenge isn’t a lack of data but the effort required to surface the right information at the right moment.