Operations | Monitoring | ITSM | DevOps | Cloud

Missing JMX metrics make it hard to know what’s happening in a Java application, especially when vague errors or configuration mismatches make the cause difficult to diagnose. In this video, you’ll see how to troubleshoot common JMX metric collection issues and isolate the cause in less time.

See how Bits Chat turns a natural-language request into an automated incident response workflow. In this demo, Bits Chat builds a workflow that investigates a monitor alert, identifies whether a recent deployment caused the issue, rolls it back when appropriate, and sends a summary to Slack.

Datadog Bits Agent Builder lets you build AI agents that use your observability data to automate operational tasks. In this walkthrough, see how to build an agent that analyzes monitor and alert activity, identifies patterns, and provides actionable recommendations to improve your monitoring strategy. With Bits Agent Builder, you can give agents access to Datadog data and tools, customize their instructions and models, and run them automatically to continuously analyze and act on your environment.

Adding metadata to Software Catalog entities manually is a tedious process that doesn’t scale as your service count grows. This video shows you how to automate that work with Terraform so you can add shared metadata across existing Software Catalog entities at scale.

In July’s This Month in Datadog, Jeremy is joined by Datadog product leaders for in-depth conversations about how Bits enables you to confidently evaluate and release features containing AI-generated code, and use natural language to ask, understand, and act across Datadog.

AssemblyAI is a leading Voice AI platform that provides speech-to-text models and AI infrastructure developers use to build real-time voice applications. AssemblyAI uses Datadog to unify observability across its AI inference pipelines and multi-cloud GPU infrastructure, enabling the team to optimize performance and costs, accelerate model releases, and confidently deliver fast, reliable AI experiences at scale.

At hyperscale, a regional cloud outage is not merely a technical disruption—for Samsung Account, which serves 2.1 billion users across three global regions, it is an immediate global service crisis. Fragmented, region-siloed monitoring creates blind spots that make early detection nearly impossible, leaving SRE teams perpetually reactive rather than predictive. The path to proactive reliability requires both a philosophical shift and a foundational change in how observability data is collected, unified, and reasoned over.

Modernizing a legacy system serving 20 million devices without users noticing is like replacing a jet engine mid-flight. In this session, YoungJin Jung and Donggen Hong from LG U+ share their 18-month journey transforming a Telco-scale API Gateway from a rigid, proprietary solution into a high-performance, open-source architecture on AWS, and the operational challenges they solved along the way.

Replace "AI shipped on hope" with an operating model that holds up once real users depend on it. AI quality is multi-dimensional, covering accuracy, tone, safety, and faithfulness to user data, and can't be debugged from outputs alone. Without visibility into what their AI actually did in production, teams miss regressions, reverse-engineer chains by hand, and watch a single bad answer erode trust built over hundreds of right ones.

Every team is doing something with AI right now. What that something is, is an entirely different question. And whether that something is successful? Most teams are still figuring it out as they go.