Dell Software released Foglight APM 5.9, which allows IT to see every step of a transaction for a complete picture of the end user’s experience with an application, combined with the exact execution path and other details through the entire application stack from the code and middleware, all the way down through the operating system and hypervisor.
While many businesses routinely gauge application availability, few have comprehensive visibility into the customer’s experience as they transact with an application. Foglight APM now links all application layer traces back to the end user, creating a single customer-centric transaction model that provides IT with actionable information to help them better understand and manage the technology that impacts business goals and customer satisfaction.
Foglight APM enables both IT and business stakeholders to speak the same language ─ with a common focus on customer experience ─ and gives IT the capability to isolate problems, and identify opportunities to proactively improve application performance.
With its Transaction DNA technology, the new release of Foglight APM can also facilitate improved collaboration between IT operations and developers. This unique technology unifies disparate data sources under a common business-oriented framework, enabling the two groups to work on the same problem as a team to resolve it quickly and help alleviate blamestorms. Improved collaboration is key to reducing Mean Time to Resolution, and is a business priority today because of its impact on critical business process transactions and users conducting commerce with web applications.
Foglight APM 5.9 includes the following new capabilities:
- Enhanced support for monitoring of AJAX applications: Combines data collected from both the network and within the browser, to continuously report on 100 percent of user session activity, including performance breakdown and errors, navigation timing, and keyboard/mouse events.
- Unique Transaction DNA technology: Delivers a data model that uses transactions as the framework for unifying disparate sources of IT data for dashboards, visualizations and analysis. All application layer traces are linked back to the end user and their associated session activity.
- Interactive pre-defined page content analysis: Breakdown by location, browser and content type makes it easy for IT and business analysts alike to pivot user activity data to understand common attributes of application performance slowdowns and errors.
- Powerful “funnel” analysis of multi-step transactions: Links directly back to data, to uniquely provide technical evidence for the impact on business transaction completion rates from factors such as web design, performance and more.
- Transaction Trace Repository for storage and analysis of high volume, high granularity data: Specially designed to address the Big Data problem in APM by capturing all performance and content details for every click by every web user. A turnkey appliance (either physical or virtual) provides a single point of management, that is fast to install, easy to maintain, and scales to support the largest user volumes, with comprehensive security and compliance adherence.
John Newsom, executive director, Application Performance Monitoring, Dell Software, said: “Foglight APM 5.9 represents a significant breakthrough in collaborative customer-centric APM, with user behavior monitoring, end-to-end transaction analysis and contextual forensics that provide critical insight for both IT and the business. The best measure of application delivery is the end-user experience, and Foglight APM offers a more dynamic, real-time, transaction-centric view of the entire application, including every aspect of every end user interaction."
"With Foglight, application support teams can see the exact end user experience, and isolate problems to application code within the browser or on the application server, external third party service calls, the application infrastructure, or anywhere else in the execution path. When both IT and business analysts are equipped with answers, rather than data, MTTR is drastically reduced and end-user experience is optimized.”
Related Links:
The Latest
Like most digital transformation shifts, organizations often prioritize productivity and leave security and observability to keep pace. This usually translates to both the mass implementation of new technology and fragmented monitoring and observability (M&O) tooling. In the era of AI and varied cloud architecture, a disparate observability function can be dangerous. IT teams will lack a complete picture of their IT environment, making it harder to diagnose issues while slowing down mean time to resolve (MTTR). In fact, according to recent data from the SolarWinds State of Monitoring & Observability Report, 77% of IT personnel said the lack of visibility across their on-prem and cloud architecture was an issue ...
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 ...
The observability industry has an evolving relationship with AI. We're not skeptics, but it's clear that trust in AI must be earned ... In Grafana Labs' annual Observability Survey, 92% said they see real value in AI surfacing anomalies before they cause downtime. Another 91% endorsed AI for forecasting and root cause analysis. So while the demand is there, customers need it to be trustworthy, as the survey also found that the practitioners most enthusiastic about AI are also the most insistent on explainability ...
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 ...