AppDynamics, a Cisco company, unveiled AppDynamics for Cisco Application Centric Infrastructure (ACI), a monitoring solution with end-to-end transaction tracing and correlated data models that pinpoint which constructs in the network are impacting application performance.
Now network and application teams have a faster path to remediation and for the first time can understand how network policies impact application performance.
Today, AppDynamics is bringing together it’s own application intelligence and Cisco ACI’s software defined networking solution (SDN) - to unlock business value by ensuring business-critical applications run flawlessly with integrated application and network level visibility. With AppDynamics for ACI, Cisco and AppDynamics provide an integrated view from the application code to the underlying network to deliver flawless performance and user experience.
“The running joke in IT is if there’s a problem that can’t be identified ‘it’s gotta be the network’” said Danny Winokur, CPO, AppDynamics. “Combining AppDynamics’ deep application insight with Cisco’s network expertise gives enterprises the fastest root cause analysis, reduced risk of unexpected application outages and greater trust across teams, leading to better customer experiences and business results.”
Only with AppDynamics for Cisco ACI are enterprises able to follow a transaction through the application and deep into the network. By correlating application and network data models, app and network teams have the fastest path to remediation by pinpointing exactly which constructs in the network are impacting application performance. With AppDynamics’ for Cisco ACI, application and network teams benefit from:
Fastest root cause analysis- AppDynamics for ACI empowers operation teams to find the root cause of incidents in minutes, from the application to the network. Teams can now spend time fixing issues instead of endless hours searching for the problem. When the network is at fault, teams can quickly drill down from the application symptoms to the underlying network endpoints to remediate the problem and restore access with minimal disruption to the end user.
Reduced risk of unexpected application outages- When NetOps make any change in the network configuration through APIC, they can now see the exact impact that change in policy has on application performance, providing critical visibility to avoid unexpected slowdowns. This is only possible because NetOps team can now directly see how their changes impact application performance without leaving ACI.
AppDynamics for ACI is available today with an Advanced APM license. Visibility of application node health within ACI is available as a beta.
The Latest
In the world of digital-first business, there is no tolerance for service outages. Businesses know that outages are the quickest way to lose money and customers. For smaller organizations, unplanned downtime could even force the business to close ... A new study from PagerDuty, The State of AI-First Operations, reveals that companies actively incorporating AI into operations now view operational resilience as a growth driver rather than a cost center. But how are they achieving it? ...
In live financial environments, capital markets software cannot pause for rebuilds. New capabilities are introduced as stacked technology layers to meet evolving demands while systems remain active, data keeps moving, and controls stay intact. AI is no exception, and its opportunities are significant: accelerated decision cycles, compressed manual workflows, and more effective operations across complex environments. The constraint isn't the models themselves, but the architectural environments they enter ...
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 ...