AppDynamics, a Cisco company, unveiled Cognition Engine, the company's next generation of application performance monitoring.
Cognition Engine builds on AppDynamics’ baseline, transaction snapshots and business analytics to deliver application performance diagnostics and automated root-cause analysis that has the ability to reduce resolution times from minutes to seconds. Cognition Engine extends AIOps to the application, introducing new levels of insight that empower modern businesses with a competitive edge in the digital economy.
“Cognition Engine turns traditional APM on its head. Rather than chasing symptoms, the top suspects for root cause are automatically surfaced. That level of insight completely changes the game for IT, freeing them of tedious tasks and empowering them to focus on creating business impact,” said JF Huard, CTO Data Science at AppDynamics. “Cognition Engine ultimately empowers enterprises to embrace an AIOps mindset – valuing proaction over reaction, answers over investigation and, most importantly, never losing focus on customer experience or business performance.”
At Cognition Engine’s core is AppDynamics’ Business Transaction and machine learning expertise from Perspica, which joined the AppDynamics team in 2017. The integration of these technologies is the foundation of AppDynamics Cognition Engine:
- Anomaly Detection: Cognition Engine constantly evolves, training and evaluating data in real-time as it enters the system using streaming analytics technology. It ingests, processes, and analyzes millions of records per second automatically understanding how metrics correlate and instantly knowing when there’s a break in correlation among datasets. Because it automatically evaluates healthy behavior, there is minimal need for user configuration out of the box. Problems can be detected in minutes, even hours before systems that rely on traditional thresholding, giving IT a head start on the fixing the problem before its customers notice.
- Automated Root Cause Analysis: To alleviate hours of tedious manual analysis spent on curated data, Cognition Engine automatically isolates metrics that deviate from normal behavior, as determined by machine-learned correlation, to present the top suspects of root cause for any application issue. By clearly showing the impact of those deviating metrics on the poor performance for mission critical Business Transactions, it drastically reduces resources spent on identifying root causes and the need for experience in performance monitoring.
Cognition Engine is available to SaaS customers at no additional cost.
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 ...