Operations | Monitoring | ITSM | DevOps | Cloud

Remember being excited (or dreading, depending on the stage of your career and the company you worked at) about writing unit tests? Or sweating all the details in your end-to-end and integration tests you were sure covered all the use cases your users would hit? These days a lot of UIs are slowly being replaced by a single input field and an agent that promises to deliver the same value a UI would, but with the elegance and pun-ness of a “Jarvis”.

Engineering teams spend much of their incident response time investigating the problem and coordinating the response. Both tasks become harder when telemetry data lives in one place, deployment history is stored in another, and conversations unfold across chat channels and incident bridges. Responders often spend the first part of an incident rebuilding context before they can begin testing hypotheses and working toward resolution.

At Datadog, we want our developers to become better at using AI tools with the end goal of building quality software, faster, that generates real value. This includes not only the products and features that our customers use, but also the internal tools that help keep our workflows running smoothly behind the scenes.

The World Cup may be the most-watched event in media history — so what does it look like from inside the network? We dug into ISP traffic data to reveal how Fox Sports peaks during US games, why second halves usually win, and how traffic flows shift for entire nations like Brazil and Iran when their team takes the field.

Summary: Q2 was about giving teams more leverage with less overhead. Between April and June 2026, releases across Telegraf, InfluxDB 3, and InfluxDB 3 Explorer focused on reducing manual work and putting more control directly in their hands as they scale. Telegraf Enterprise reached general availability, giving teams a centralized way to manage, monitor, and support tens of thousands of Telegraf agents.

For the last two years, enterprise AI strategy has largely focused on one thing: adoption. Organizations encouraged employees to experiment with ChatGPT, Claude, Copilot, Gemini, and dozens of emerging AI tools in the hope that productivity gains would naturally follow. CIOs approved pilots, departments launched AI task forces, and leaders pushed teams to integrate AI into everyday work as quickly as possible. But the enterprise AI conversation is beginning to change.

The Agile software development lifecycle was designed to surface problems early, with short sprints, iterative testing, and continuous integration built on the premise that faster feedback loops produce better software. AI coding tools have changed the velocity equation across every phase of that loop, but the phases designed to catch failures are struggling to keep up because build speed and validation capacity have not accelerated at the same rate, and the gap between them is widening with every sprint.