Skip to main content

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

Most organizations approach OpenTelemetry as a collection of individual tools they need to assemble from scratch. This view misses the bigger picture. OpenTelemetry is a complete telemetry framework with composable components that address specific problems at different stages of organizational maturity. You start with what you need today and adopt additional pieces as your observability practices evolve ...

One of the earliest lessons I learned from architecting throughput-heavy services is that simplicity wins repeatedly: fewer moving parts, loosely coupled execution (fewer synchronous calls), and precise timing metering. You want data and decisions to travel the shortest possible path. The goal is to build a system where every strategy and each line of code (contention is the key metric) complements the decision trees ...

As discussions around AI "autonomous coworkers" accelerate, many industry projections assume that agents will soon operate alongside human staff in making decisions, taking actions, and managing tasks with minimal oversight. But a growing number of critics (including some of the developers building these systems) argue that the industry still has a long way to go to be able to treat AI agents like fully trusted teammates ...

Enterprise AI has entered a transformational phase where, according to Digitate's recently released survey, Agentic AI and the Future of Enterprise IT, companies are moving beyond traditional automation toward Agentic AI systems designed to reason, adapt, and collaborate alongside human teams ...

The numbers back this urgency up. A recent Zapier survey shows that 92% of enterprises now treat AI as a top priority. Leaders want it, and teams are clamoring for it. But if you look closer at the operations of these companies, you see a different picture. The rollout is slow. The results are often delayed. There's a disconnect between what leaders want and what their technical infrastructure can handle ...

Kyndryl's 2025 Readiness Report revealed that 61% of global business and technology leaders report increasing pressure from boards and regulators to prove AI's ROI. As the technology evolves and expectations continue to rise, leaders are compelled to generate and prove impact before scaling further. This will lead to a decisive turning point in 2026 ...

Cloudflare's disruption illustrates how quickly a single provider's issue cascades into widespread exposure. Many organizations don't fully realize how tightly their systems are coupled to thirdparty services, or how quickly availability and security concerns align when those services falter ... You can't avoid these dependencies, but you can understand them ...

If you work with AI, you know this story. A model performs during testing, looks great in early reviews, works perfectly in production and then slowly loses relevance after operating for a while. Everything on the surface looks perfect — pipelines are running, predictions or recommendations are error-free, data quality checks show green; yet outcomes don't meet the ground reality. This pattern often repeats across enterprise AI programs. Take for example, a mid-sized retail banking and wealth-management firm with heavy investments in AI-powered risk analytics, fraud detection and personalized credit-decisioning systems. The model worked well for a while, but transactions increased, so did false positives by 18% ...

Basic uptime is no longer the gold standard. By 2026, network monitoring must do more than report status, it must explain performance in a hybrid-first world. Networks are no longer just static support systems; they are agile, distributed architectures that sit at the very heart of the customer experience and the business outcomes ... The following five trends represent the new standard for network health, providing a blueprint for teams to move from reactive troubleshooting to a proactive, integrated future ...

APMdigest's Predictions Series concludes with 2026 AI Predictions — industry experts offer predictions on how AI and related technologies will evolve and impact business in 2026. Part 5, the final installment, covers AI's impacts on IT teams ...

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

Most organizations approach OpenTelemetry as a collection of individual tools they need to assemble from scratch. This view misses the bigger picture. OpenTelemetry is a complete telemetry framework with composable components that address specific problems at different stages of organizational maturity. You start with what you need today and adopt additional pieces as your observability practices evolve ...

One of the earliest lessons I learned from architecting throughput-heavy services is that simplicity wins repeatedly: fewer moving parts, loosely coupled execution (fewer synchronous calls), and precise timing metering. You want data and decisions to travel the shortest possible path. The goal is to build a system where every strategy and each line of code (contention is the key metric) complements the decision trees ...

As discussions around AI "autonomous coworkers" accelerate, many industry projections assume that agents will soon operate alongside human staff in making decisions, taking actions, and managing tasks with minimal oversight. But a growing number of critics (including some of the developers building these systems) argue that the industry still has a long way to go to be able to treat AI agents like fully trusted teammates ...

Enterprise AI has entered a transformational phase where, according to Digitate's recently released survey, Agentic AI and the Future of Enterprise IT, companies are moving beyond traditional automation toward Agentic AI systems designed to reason, adapt, and collaborate alongside human teams ...

The numbers back this urgency up. A recent Zapier survey shows that 92% of enterprises now treat AI as a top priority. Leaders want it, and teams are clamoring for it. But if you look closer at the operations of these companies, you see a different picture. The rollout is slow. The results are often delayed. There's a disconnect between what leaders want and what their technical infrastructure can handle ...

Kyndryl's 2025 Readiness Report revealed that 61% of global business and technology leaders report increasing pressure from boards and regulators to prove AI's ROI. As the technology evolves and expectations continue to rise, leaders are compelled to generate and prove impact before scaling further. This will lead to a decisive turning point in 2026 ...

Cloudflare's disruption illustrates how quickly a single provider's issue cascades into widespread exposure. Many organizations don't fully realize how tightly their systems are coupled to thirdparty services, or how quickly availability and security concerns align when those services falter ... You can't avoid these dependencies, but you can understand them ...

If you work with AI, you know this story. A model performs during testing, looks great in early reviews, works perfectly in production and then slowly loses relevance after operating for a while. Everything on the surface looks perfect — pipelines are running, predictions or recommendations are error-free, data quality checks show green; yet outcomes don't meet the ground reality. This pattern often repeats across enterprise AI programs. Take for example, a mid-sized retail banking and wealth-management firm with heavy investments in AI-powered risk analytics, fraud detection and personalized credit-decisioning systems. The model worked well for a while, but transactions increased, so did false positives by 18% ...

Basic uptime is no longer the gold standard. By 2026, network monitoring must do more than report status, it must explain performance in a hybrid-first world. Networks are no longer just static support systems; they are agile, distributed architectures that sit at the very heart of the customer experience and the business outcomes ... The following five trends represent the new standard for network health, providing a blueprint for teams to move from reactive troubleshooting to a proactive, integrated future ...

APMdigest's Predictions Series concludes with 2026 AI Predictions — industry experts offer predictions on how AI and related technologies will evolve and impact business in 2026. Part 5, the final installment, covers AI's impacts on IT teams ...