Dynatrace extended the Dynatrace platform’s release validation capabilities to automatically deliver user experience validation and user experience assurance (UXA) at every stage of the software development lifecycle (SDLC).
By automatically incorporating user experience insights – including application availability, performance, and feature engagement – the Dynatrace platform allows DevOps and SRE teams to continuously evaluate their applications against specific, measurable, and achievable service level objectives (SLOs). This empowers teams to automatically improve software quality and resiliency at any scale with significantly less manual effort.
These enhancements to the Dynatrace platform leverage data from simulated software testing processes that mimic real user engagement and end-to-end transactions. Davis, the AI engine at the platform’s core, automatically combines these test results with additional, comprehensive observability data to deliver instant and precise answers about users’ interactions with applications.
“Without an actionable understanding of the user experience, DevOps and SRE teams can’t meet the demand to accelerate innovation, maintain software quality and security, and deliver competitive digital services,” said Steve Tack, SVP of Product Management, Dynatrace. “This latest enhancement to Dynatrace automates time-consuming release validation processes and delivers immediate and precise insights into how users will experience an application once it’s live. Combining this functionality with the platform’s extensive integrations with key DevSecOps tools enables teams to drive higher throughput, ensure the highest performance and security standards, and innovate with confidence to ensure a competitive edge.”
These capabilities will be generally available within 90 days of this announcement.
The Latest
For many B2B and B2C enterprise brands, technology isn't a core strength. Relying on overly complex architectures (like those that follow a pure MACH doctrine) has been flagged by industry leaders as a source of operational slowdown, creating bottlenecks that limit agility in volatile market conditions ...
FinOps champions crucial cross-departmental collaboration, uniting business, finance, technology and engineering leaders to demystify cloud expenses. Yet, too often, critical cost issues are softened into mere "recommendations" or "insights" — easy to ignore. But what if we adopted security's battle-tested strategy and reframed these as the urgent risks they truly are, demanding immediate action? ...
Two in three IT professionals now cite growing complexity as their top challenge — an urgent signal that the modernization curve may be getting too steep, according to the Rising to the Challenge survey from Checkmk ...
While IT leaders are becoming more comfortable and adept at balancing workloads across on-premises, colocation data centers and the public cloud, there's a key component missing: connectivity, according to the 2025 State of the Data Center Report from CoreSite ...
A perfect storm is brewing in cybersecurity — certificate lifespans shrinking to just 47 days while quantum computing threatens today's encryption. Organizations must embrace ephemeral trust and crypto-agility to survive this dual challenge ...
In MEAN TIME TO INSIGHT Episode 14, Shamus McGillicuddy, VP of Research, Network Infrastructure and Operations, at EMA discusses hybrid multi-cloud network observability...
While companies adopt AI at a record pace, they also face the challenge of finding a smart and scalable way to manage its rapidly growing costs. This requires balancing the massive possibilities inherent in AI with the need to control cloud costs, aim for long-term profitability and optimize spending ...
Telecommunications is expanding at an unprecedented pace ... But progress brings complexity. As WanAware's 2025 Telecom Observability Benchmark Report reveals, many operators are discovering that modernization requires more than physical build outs and CapEx — it also demands the tools and insights to manage, secure, and optimize this fast-growing infrastructure in real time ...
As businesses increasingly rely on high-performance applications to deliver seamless user experiences, the demand for fast, reliable, and scalable data storage systems has never been greater. Redis — an open-source, in-memory data structure store — has emerged as a popular choice for use cases ranging from caching to real-time analytics. But with great performance comes the need for vigilant monitoring ...
Kubernetes was not initially designed with AI's vast resource variability in mind, and the rapid rise of AI has exposed Kubernetes limitations, particularly when it comes to cost and resource efficiency. Indeed, AI workloads differ from traditional applications in that they require a staggering amount and variety of compute resources, and their consumption is far less consistent than traditional workloads ... Considering the speed of AI innovation, teams cannot afford to be bogged down by these constant infrastructure concerns. A solution is needed ...