Operations | Monitoring | ITSM | DevOps | Cloud

October’s This Month in Datadog comes to you from the Datadog Summit in San Francisco, where Jeremy shares a look inside the event, with our latest product updates. Plus, we cover how to: Build and manage Datadog Workflows using natural language, turning descriptions of automation tasks into workflows that teams can review before publishing Detect and investigate Kafka issues with Kafka Console, bringing health and performance data into one place Turn raw logs into structured fields with Datadog Tap, making logs easier to search and analyze.

AI has become incredibly sophisticated and changed the way we build and power applications. But it has also introduced volatile complexity that challenges how we ensure reliability and protect user experiences. Join our panel of experts as they discuss the realities of building AI-powered applications. They’ll talk about how testing has evolved, share insights into how they do evals and experiments, and offer advice on how to get better visibility into how your AI is performing.

As Datadog scaled to handle over a hundred trillion daily events, our engineering teams built their own custom query engines to keep things fast. While this helped teams move quickly, it also created silos that made it tough to analyze data across different products. To keep growing and support AI, we had to find a way to bring these engines together using open standards without slowing anyone down or risking reliability.

Join Datadog's Chief Product Officer Yanbing Li and OpenAI's Member of Product Staff Alexander Embiricos as they discuss emerging technologies and innovation, how they impact businesses today, and the new opportunities they create for you.

Automate Slack support questions with Datadog Workflow Automation and Bits AI Agent Builder. Datadog Workflow Automation can now trigger directly from messages posted in Slack. In this quick demo, Yuta Yoshida, Product Manager at Datadog, shows how to build an automated support workflow that: → Detects new support questions in a Slack channel→ Sends questions to a custom agent built with Bits AI Agent Builder→ Replies directly in the Slack thread with an answer and relevant documentation.

See how Datadog Journey Monitoring brings together user behavior, real-user insights, and proactive testing to track the health of critical user journeys, understand business impact, and quickly troubleshoot issues.

Modal is an AI infrastructure company rebuilding the cloud for AI, giving developers easy access to the compute needed to build and scale AI applications without managing the underlying complexity. Operating across more than 20 cloud providers and launching approximately 26 million containers daily, Modal relies on Datadog to unify observability across its infrastructure, applications, networking, and CI—helping its engineers detect 99% of incidents within five minutes, resolve 96% of issues before customer impact, and confidently scale one of the world’s most complex AI platforms.

Engineering ships faster than ever with AI writing more of the code, but test coverage has to keep pace, or users find the broken flows first.

AI can write code, but how does it do with a large, organically built codebase with years of development? Datadog’s monolithic webapp had significant scale and support challenges, so we knew the time had come to break it up. But when the code has been written, revised and tweaked so many times (as we keep adding new features and capabilities), by so many teams, that becomes much harder.

Incident response often spans a patchwork of tools, handoffs, and manual steps. An engineer gets paged, a team investigates the issue, the incident commander coordinates the response, SREs document decisions, and somebody (anybody!) writes the postmortem. Every handoff introduces more context switching, increasing the risk of critical information loss, and ultimately slowing resolution.

When production breaks, the clues are everywhere but the story is scattered. RUM sessions, APM traces, metrics, and logs allow Datadog to tell you part of the story. What’s missing is your service ownership quirks, your team's conventions, or the three-year-old workaround buried in a comment that explains why a certain endpoint behaves the way it does.

Senior engineers are the pillars of SRE programs—not only do they have the leadership to manage incidents well, they also have the tribal knowledge to understand the technical causes of tricky issues. But when the know-how is confined to a few individuals, it greatly limits the effectiveness of your SRE team.

AI is generating code at 10x human speed, but we’re not shipping software 10x faster. Agents have a quality problem, and we still can’t trust their output without validation. Breaking the code review model didn’t help. So what would it take to autonomously ship code that’s actually trustworthy? Join us to explore what an agentic harness looks like when the SDLC is the harness, a validation system that spans intent, CI, staging, and production, with context flowing bidirectionally between all stages.

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, Senior Vice President of Product Management, and Cansu Berkem, Director of 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.