Catchpoint announced two AI-powered capabilities designed to simplify digital resilience for critical applications: Catchpoint Root Cause Analysis (RCA) and Catchpoint Advisor, which improve monitoring posture and bring immediate insights into IT incidents, ending the guesswork.
These new capabilities in the Catchpoint platform directly address critical pain points, starting with actionable guidance based on best practices, ensuring true user-to-code coverage, and enabling instant problem detection and root cause isolation.
Catchpoint Root Cause Analysis: The new RCA capability quickly identifies outages and pinpoints the primary service responsible for an issue, reporting it in clear text without requiring teams to manually inspect every dependency. Built on Catchpoint’s Internet Stack Map dependency map and powered by event intelligence, RCA contextualizes service disruptions by automatically analyzing backend waterfall data.
Key Benefits: Early warning signal, faster identification of problems across complex service stacks. IT teams not only know that a problem is happening but what is the likely culprit. Available to all Catchpoint customers at no additional cost.
Catchpoint Advisor: Catchpoint Advisor provides recommendations that guide IT ops teams with best practices to ensure their monitoring strategy covers critical services and provides the visibility needed to increase service resilience for each application. As customers take advantage of Stack Map to get visibility into all the internal and external dependencies for a service, Catchpoint’s AI engine recommends the right mix of tests, Internet Sonar coverage, and new pre-configured tests with alerts.
Key Benefits: Accelerates monitoring setup, ensures complete coverage of dependencies affecting an application or system, and eliminates blind spots.
Feature Details:
- Suggests adding existing and new tests to the dependency map for better coverage.
- Recommends adding Internet Sonar services for services owned, and those not yet being monitored.
- Pre-configures recommended test types including HTTP Web, Web Chrome (Playwright), SSL, Traceroute, DNS Experience, DNS Direct, and more.
- Highlights existing monitoring assets and identifies gaps.
Recommendations included at no cost; standard charges apply for any new Sonar or test additions.
“AI should remove complexity, not add to it,” said Mehdi Daudi, Catchpoint co-founder and CEO. “With these new AI-powered capabilities, Catchpoint is making it dramatically easier for organizations to achieve proactive visibility across their critical applications. By embedding clear, actionable intelligence, we’re ensuring customers can identify and resolve issues faster than ever before — without guesswork.”
These new AI capabilities reinforce Catchpoint’s commitment to delivering digital resilience for the world’s most critical online services. With automated intelligence driving faster diagnosis and smarter monitoring, organizations gain the confidence to ensure peak performance for every user, every time.
“There is a lot of AI-washing in the industry, we do not want to add AI capabilities just to check the box, “ said Matt Izzo, Chief Product Officer at Catchpoint, “We have been investing in AI for many years now, with a very clear focus on the practical value of this capabilities: improving resilience of complex distributed applications and making life easier for IT operations teams.”
The Latest
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 ...
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.