Riverbed Technology announced the availability of Riverbed OPNET AppInternals Xpert 8.5, a unified application performance management (APM) suite that provides deep visibility into the performance of complex, multi-tier applications.
The new features in OPNET AppInternals Xpert 8.5 combine to give complete visibility into the performance of applications, from the browser to backend databases.
By integrating end-user experience monitoring with code-level transaction tracing and monitoring of application and system performance metrics, application support teams and developers now have the information they need to more quickly and collaboratively identify, troubleshoot, and debug application performance issues.
With the introduction of OPNET AppInternals Xpert 8.5, application support, development, and QA teams have complete and coordinated visibility into application performance and, therefore, can proactively detect issues before they impact the business as well as analyze applications prior to deployment to identify bottlenecks that can impact performance in production.
In addition, organizations deploying OPNET AppInternals Xpert 8.5 can take advantage of other Riverbed technologies, including Riverbed Stingray® Traffic Manager, to gain added visibility into end user experiences.
Service-oriented architecture and agile development processes are not only increasing the rate at which new applications are rolled out, but also causing applications to sprawl and increase in complexity. New features in OPNET AppInternals Xpert 8.5 combine to provide application support teams and developers with the information they need to quickly identify and address complex, multi-tier application performance issues.
OPNET AppInternals Xpert now integrates with popular integrated development environments (IDE), including Microsoft Visual Studio and Eclipse. This integration enables bi-directional drill down to streamline application debugging and troubleshooting, and opens up visibility and enhances collaboration across application operations and developer teams.
For example, operations teams can drill down from the transaction trace to the affected code to highlight the affected calls or methods for developers. Developers and QA staff can also quickly debug new releases by understanding where code is being used and who is using it in the production environment.
OPNET AppInternals Xpert 8.5 integrates real end-user experience (EUE) monitoring with transaction tracing, and application component data-adding visibility into the front-end user experience and tying it back to the exact transaction responsible for the poor experience.
This integration enables application support teams to start troubleshooting with an accurate end-to-end view of application performance from the user's perspective. As a result, these teams are aware as soon as a problem occurs and can use this end-user experience and transaction information to guide a "deep dive" into the application transaction and specific code.
End-user experience monitoring of web-based applications, including cloud-based applications, is achieved by leveraging lightweight JavaScript which can now be automatically injected via AppInternals agents or Stingray Traffic Manager, an application delivery controller that allows organizations to build custom functionality or implement traffic management policies that are unique to an application.
"Whether organizations are deploying new applications, consolidating or virtualizing data centers, or migrating to the cloud, managing application performance requires a holistic view that includes end-user experience monitoring, application transaction tracing and component monitoring, and underlying infrastructure and network performance management," said Paul Brady, Sr VP and GM of the Riverbed Performance Management Business Unit. "OPNET AppInternals Xpert provides our customers with a fast path to application problem resolution by delivering the actionable insight and data that application support and developer teams need to deliver the application and business performance."
OPNET AppInternals Xpert 8.5 is available now.
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 ...