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Catchpoint Announces Performance and Resilience Monitoring for AI Assistants and Agentic AI Systems

Catchpoint announced new capabilities—Performance and Resilience Monitoring for AI Assistants and Agentic AI systems—to proactively ensure uptime, speed, and reliability for mission-critical AI-driven workflows.

These new features empower organizations to proactively monitor both Agentic AI systems and AI-powered assistants with confidence.

Catchpoint’s new capabilities ensure immediate visibility into AI performance, enabling proactive management of disruptions to protect business continuity and customer experience.

AI Assistant Reliability Monitoring enables organizations to proactively detect and resolve issues affecting AI APIs, LLMs, and chatbots. 

Key capabilities include:

  • Global API reachability: Test AI endpoints from key global regions to rapidly detect DNS, routing, or regional outages from thousands of intelligent agents in over 100 countries.
  • Latency baselines: Continuously track response times to catch slowdowns before user experiences degrade.
  • Synthetic prompt monitoring: Simulate real-world interactions to validate response accuracy and consistency.
  • Uptime and error detection: Instantly alert on API downtime, errors, overload conditions, or malformed responses.
  • Visual dependency mapping: Get the full context of the entire system or application to understand any component that may be impacting user experience, not only AI.

Agentic AI Resilience Monitoring: Designed specifically for complex, autonomous AI workflows that rely on multiple external dependencies, the new capability delivers full-stack visibility and observability across APIs, networks, cloud services, and third-party tools. 

Features include:

  • Third-party API Monitoring: Track stability and latency of critical cloud services, SaaS APIs, and databases.
  • Multi-hop Dependency Visibility: Trace the root cause of cascading failures across complex AI workflows.
  • CI/CD Monitoring Automation: Automatically integrate monitoring into CI/CD pipelines to test changes in AI infrastructure.
  • Cloud Region Resilience: Identify and mitigate risks associated with specific cloud region disruptions and performance issues.
  • Global performance testing: from anywhere in the global observability network or private intelligent agents deployed in key locations, data centers, or offices.

“AI assistants and agentic agents are only as reliable as the networks and APIs they depend on,” said Mehdi Daoudi, CEO of Catchpoint. “Our new capabilities give organizations the visibility they need to ensure AI resilience, reduce downtime, and deliver exceptional digital experiences, enabling IT organizations to innovate as they build the future.”

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Ask an operations team what they monitor and you will hear a familiar list: latency, errors, throughput, uptime. Those signals still matter for AI, but they no longer answer the most important question. As Krishna Sai, CTO of SolarWinds, puts it: "Traditional observability tells you a system is running. AI observability tells you whether it's still reasoning soundly. Those are completely different questions" ...

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Catchpoint Announces Performance and Resilience Monitoring for AI Assistants and Agentic AI Systems

Catchpoint announced new capabilities—Performance and Resilience Monitoring for AI Assistants and Agentic AI systems—to proactively ensure uptime, speed, and reliability for mission-critical AI-driven workflows.

These new features empower organizations to proactively monitor both Agentic AI systems and AI-powered assistants with confidence.

Catchpoint’s new capabilities ensure immediate visibility into AI performance, enabling proactive management of disruptions to protect business continuity and customer experience.

AI Assistant Reliability Monitoring enables organizations to proactively detect and resolve issues affecting AI APIs, LLMs, and chatbots. 

Key capabilities include:

  • Global API reachability: Test AI endpoints from key global regions to rapidly detect DNS, routing, or regional outages from thousands of intelligent agents in over 100 countries.
  • Latency baselines: Continuously track response times to catch slowdowns before user experiences degrade.
  • Synthetic prompt monitoring: Simulate real-world interactions to validate response accuracy and consistency.
  • Uptime and error detection: Instantly alert on API downtime, errors, overload conditions, or malformed responses.
  • Visual dependency mapping: Get the full context of the entire system or application to understand any component that may be impacting user experience, not only AI.

Agentic AI Resilience Monitoring: Designed specifically for complex, autonomous AI workflows that rely on multiple external dependencies, the new capability delivers full-stack visibility and observability across APIs, networks, cloud services, and third-party tools. 

Features include:

  • Third-party API Monitoring: Track stability and latency of critical cloud services, SaaS APIs, and databases.
  • Multi-hop Dependency Visibility: Trace the root cause of cascading failures across complex AI workflows.
  • CI/CD Monitoring Automation: Automatically integrate monitoring into CI/CD pipelines to test changes in AI infrastructure.
  • Cloud Region Resilience: Identify and mitigate risks associated with specific cloud region disruptions and performance issues.
  • Global performance testing: from anywhere in the global observability network or private intelligent agents deployed in key locations, data centers, or offices.

“AI assistants and agentic agents are only as reliable as the networks and APIs they depend on,” said Mehdi Daoudi, CEO of Catchpoint. “Our new capabilities give organizations the visibility they need to ensure AI resilience, reduce downtime, and deliver exceptional digital experiences, enabling IT organizations to innovate as they build the future.”

The Latest

For decades, traditional observability tools have forced IT professionals to sift through endless dashboards, turning a flood of alerts into actionable insight and driving widespread burnout. While enterprises adopted AI to eliminate this friction, most still deploy it reactively rather than proactively ... As systems become more complex and IT moves to the center of business strategy, a reactive posture is unsustainable ...

Ask an operations team what they monitor and you will hear a familiar list: latency, errors, throughput, uptime. Those signals still matter for AI, but they no longer answer the most important question. As Krishna Sai, CTO of SolarWinds, puts it: "Traditional observability tells you a system is running. AI observability tells you whether it's still reasoning soundly. Those are completely different questions" ...

AI observability serves several roles across an organization. AI engineers use it to build, evaluate, and improve AI systems. SREs use it to operate those systems reliably and efficiently in production, including managing their cost. Leaders need it to connect AI behavior and operational performance to business outcomes. The common need is a clear understanding of whether AI systems are delivering the intended result ...

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 ...