Skip to main content

Catchpoint Root Cause Analysis and Catchpoint Advisor Released

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

Part 4 outlines the challenges enterprise face when adopting AI observability. The main challenge is that AI complexity compounds at every stage of maturity. LLM-based applications introduce new behavior, dependencies, and quality considerations, while Agentic AI adds further failure modes as systems make decisions and take actions ...

Part 3 reports on the current state of AI observability. Most experts agree: enterprises are moving faster with AI than they are with the operational discipline needed to run it safely ...

When discussing AI Observability, one of the first questions that arises: Is this just an extension of traditional observability or is this a whole new type of tool? Part 2 compares traditional observability with AI observability ...

In this extensive 14-part series that will post over the next three weeks, experts across observability and AI will provide insight and opinions on the need for AI Observability, the challenges, the benefits, the current state, and predictions for the future ...

Amid rising complexity, organizations are moving quickly toward agent-driven operations: AgenticOps, according to The Impact of Agentic AI on Network Operations, a new report from Cisco ...

AI, automation, and enterprise software give organizations an extraordinary amount of leverage. Work that once required hours can happen in minutes. Information buried across systems can be analyzed almost instantly. Complex workflows can be coordinated across teams with far less manual intervention. That leverage creates a temptation to start with the technology ...

Everyone wants to talk about models. Compute clusters. Benchmark scores that climb a fraction of a point and make headlines anyway. It's an easy conversation to have because it's measurable. But it's also increasingly beside the point ...

For more than a decade, the default infrastructure play has been simple. Move more workloads to the cloud, retire on-premises gear and treat the data center as a cost to shrink. That logic held while public cloud offered near-limitless capacity at predictable prices, and AI demand is now testing both halves of that assumption ...

Most enterprise IT leaders can tell you exactly how many applications are running across their organization. They can surface a dashboard showing telecom spend by region, pull a report on SaaS license utilization, or call up a real-time view of mobile device inventory. The data is there, and IT leaders have largely delivered on the promise of visibility. Yet, in most organizations, that visibility has not translated into business value ...

Government agencies are largely misjudging their level of AI governance as the technology permeates their organization, according to Racing to Deploy: How Public Sector AI Adoption is Moving Faster Than the Foundations Needed to Support It, a new report from SolarWinds ...

Catchpoint Root Cause Analysis and Catchpoint Advisor Released

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

Part 4 outlines the challenges enterprise face when adopting AI observability. The main challenge is that AI complexity compounds at every stage of maturity. LLM-based applications introduce new behavior, dependencies, and quality considerations, while Agentic AI adds further failure modes as systems make decisions and take actions ...

Part 3 reports on the current state of AI observability. Most experts agree: enterprises are moving faster with AI than they are with the operational discipline needed to run it safely ...

When discussing AI Observability, one of the first questions that arises: Is this just an extension of traditional observability or is this a whole new type of tool? Part 2 compares traditional observability with AI observability ...

In this extensive 14-part series that will post over the next three weeks, experts across observability and AI will provide insight and opinions on the need for AI Observability, the challenges, the benefits, the current state, and predictions for the future ...

Amid rising complexity, organizations are moving quickly toward agent-driven operations: AgenticOps, according to The Impact of Agentic AI on Network Operations, a new report from Cisco ...

AI, automation, and enterprise software give organizations an extraordinary amount of leverage. Work that once required hours can happen in minutes. Information buried across systems can be analyzed almost instantly. Complex workflows can be coordinated across teams with far less manual intervention. That leverage creates a temptation to start with the technology ...

Everyone wants to talk about models. Compute clusters. Benchmark scores that climb a fraction of a point and make headlines anyway. It's an easy conversation to have because it's measurable. But it's also increasingly beside the point ...

For more than a decade, the default infrastructure play has been simple. Move more workloads to the cloud, retire on-premises gear and treat the data center as a cost to shrink. That logic held while public cloud offered near-limitless capacity at predictable prices, and AI demand is now testing both halves of that assumption ...

Most enterprise IT leaders can tell you exactly how many applications are running across their organization. They can surface a dashboard showing telecom spend by region, pull a report on SaaS license utilization, or call up a real-time view of mobile device inventory. The data is there, and IT leaders have largely delivered on the promise of visibility. Yet, in most organizations, that visibility has not translated into business value ...

Government agencies are largely misjudging their level of AI governance as the technology permeates their organization, according to Racing to Deploy: How Public Sector AI Adoption is Moving Faster Than the Foundations Needed to Support It, a new report from SolarWinds ...