Operations | Monitoring | ITSM | DevOps | Cloud

Measure the real impact of AI coding tools on software delivery with Datadog AI Impact

Engineering teams have rapidly adopted AI coding tools, but organizations still struggle to understand their impact. Existing dashboards focus on activity, such as daily active users, acceptance rates, or lines of generated code, but these metrics don’t answer a more important question: Are teams actually shipping more, faster, and with fewer issues?

Run your first microbuild in 5 minutes

AI coding agents produce code faster than most teams can validate it. Without a validation step between the agent and CI, every problem gets caught after the push, and feedback arrives long after the agent has lost context. Agents need consistent feedback while they’re working so that small failures get fixed locally and CI stays focused on moving code into production.

Building a Defensible AI Compliance Framework

Organizations have moved past theoretical conversations about AI adoption. Models, agents, and autonomous workflows are entering production environments. Business leaders are optimistic about potential gains in efficiency, decision support, and operational scale. Yet beneath this momentum, compliance and risk teams feel a different pressure.

AI Might Break Open Source Differently Than You Think

AI coding agents may not replace open source libraries overnight. But Adam Arellano, Field CTO at Harness, thinks models like Mythos could expose a bigger problem: finding bugs, vulnerabilities, and edge cases faster than maintainers can keep up. That might be the real threat to tools and libraries.

Ameet Talwalkar on Building the AI Research Lab

"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.

How Online Plant Identification Tools Work

Online plant identification tools work in a simple way: a user uploads a photo of a plant, the tool analyzes visible features such as leaves, stems, flowers, shape, color, and growth pattern, then compares those features with a plant database. After that, it shows the most common name and, in many cases, adds basic care recommendations.

Observability Expanding Beyond Infrastructure and Into AI Systems

Observability revolves essentially around understanding infrastructure health. This means that operations teams monitor applications, netwo0rks, database and cloud environments using familiar signals. They use logs, metrics, latency, uptime measurements, and traces. If systems remain available and the performance stays within expected thresholds, the teams have enough visibility to understand whether applications are functioning properly.

Inside the Grafana AI Team Weekly: Guards for AI Observability (May 5, 2026)

This is an excerpt from a real AI team weekly meeting where we talk about the stuff we build and occasionally also demo them! In this one, Principal Software Engineer Sven Großmann shows a new feature he's working on for AI Observability, called "guards". We're showing parts of our team meetings to build in public in some small way and give you a sneak preview of what's to come. But not all features we show may make it to production! You've been warned. :)