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Datadog Browser Tests Released

Datadog announced the general availability of Browser Tests, a software test automation platform designed for agile application development teams.

Datadog Browser Tests run continuously and adjust automatically to catch bugs when the website changes.

“Broken web applications can result in massive revenue and productivity losses for modern enterprises. And yet, because of the time required to write and update proper tests, agile development teams often trade deep testing for quick application releases,” said Gabriel-James Safar, Product Manager at Datadog. “Datadog’s Browser Tests are created in minutes and self-adjust as an application changes, which means bugs are caught before real users see them. Furthermore, deep integration with Datadog’s monitoring and analytics platform makes it quick for development teams to understand and fix the underlying issue.”

Browser Tests Features:

- Record user interactions and workflow tests without any coding

- Tests adjust themselves as an application changes without manual intervention

- Fully integrated with Datadog’s monitoring and analytics platform for end-to-end troubleshooting

“Being able to make browser monitoring a seamless process for customers has always been a challenge,” said Michael Azzoff, Distinguished Analyst, Infrastructure Solutions at Ovum. “With use of machine learning Datadog is at the innovation edge in monitoring, and with the combination of synthetic performance testing and analytics its approach is superior to traditional coding methods for browser monitoring.”

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Datadog Browser Tests Released

Datadog announced the general availability of Browser Tests, a software test automation platform designed for agile application development teams.

Datadog Browser Tests run continuously and adjust automatically to catch bugs when the website changes.

“Broken web applications can result in massive revenue and productivity losses for modern enterprises. And yet, because of the time required to write and update proper tests, agile development teams often trade deep testing for quick application releases,” said Gabriel-James Safar, Product Manager at Datadog. “Datadog’s Browser Tests are created in minutes and self-adjust as an application changes, which means bugs are caught before real users see them. Furthermore, deep integration with Datadog’s monitoring and analytics platform makes it quick for development teams to understand and fix the underlying issue.”

Browser Tests Features:

- Record user interactions and workflow tests without any coding

- Tests adjust themselves as an application changes without manual intervention

- Fully integrated with Datadog’s monitoring and analytics platform for end-to-end troubleshooting

“Being able to make browser monitoring a seamless process for customers has always been a challenge,” said Michael Azzoff, Distinguished Analyst, Infrastructure Solutions at Ovum. “With use of machine learning Datadog is at the innovation edge in monitoring, and with the combination of synthetic performance testing and analytics its approach is superior to traditional coding methods for browser monitoring.”

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In MEAN TIME TO INSIGHT Episode 23, Shamus McGillicuddy, VP of Research, Network Infrastructure and Operations, at EMA discusses the NetOps labor shortage ... 

Technology management is evolving, and in turn, so is the scope of FinOps. The FinOps Foundation recently updated their mission statement from "advancing the people who manage the value of cloud" to "advancing the people who manage the value of technology." This seemingly small change solidifies a larger evolution: FinOps practitioners have organically expanded to be focused on more than just cloud cost optimization. Today, FinOps teams are largely — and quickly — expanding their job descriptions, evolving into a critical function for managing the full value of technology ...

Enterprises are under pressure to scale AI quickly. Yet despite considerable investment, adoption continues to stall. One of the most overlooked reasons is vendor sprawl ... In reality, no organization deliberately sets out to create sprawling vendor ecosystems. More often, complexity accumulates over time through well-intentioned initiatives, such as enterprise-wide digital transformation efforts, point solutions, or decentralized sourcing strategies ...

Nearly every conversation about AI eventually circles back to compute. GPUs dominate the headlines while cloud platforms compete for workloads and model benchmarks drive investment decisions. But underneath that noise, a quieter infrastructure challenge is taking shape. The real bottleneck in enterprise AI is not processing power, it is the ability to store, manage and retrieve the relentless volumes of data that AI systems generate, consume and multiply ...

The 2026 Observability Survey from Grafana Labs paints a vivid picture of an industry maturing fast, where AI is welcomed with careful conditions, SaaS economics are reshaping spending decisions, complexity remains a defining challenge, and open standards continue to underpin it all ...

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In the modern enterprise, the conversation around AI has moved past skepticism toward a stage of active adoption. According to our 2026 State of IT Trends Report: The Human Side of Autonomous AI, nearly 90% of IT professionals view AI as a net positive, and this optimism is well-founded. We are seeing agentic AI move beyond simple automation to actively streamlining complex data insights and eliminating the manual toil that has long hindered innovation. However, as we integrate these autonomous agents into our ecosystems, the fundamental DNA of the IT role is evolving ...

AI workloads require an enormous amount of computing power ... What's also becoming abundantly clear is just how quickly AI's computing needs are leading to enterprise systems failure. According to Cockroach Labs' State of AI Infrastructure 2026 report, enterprise systems are much closer to failure than their organizations realize. The report ... suggests AI scale could cause widespread failures in as little as one year — making it a clear risk for business performance and reliability.

The quietest week your engineering team has ever had might also be its best. No alarms going off. No escalations. No frantic Teams or Slack threads at 2 a.m. Everything humming along exactly as it should. And somewhere in a leadership meeting, someone looks at the metrics dashboard, sees a flat line of incidents and says: "Seems like things are pretty calm over there. Do we really need all those people?" ... I've spent many years in engineering, and this pattern keeps repeating ...

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