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ITRS Introduces AI-Powered SRE Agents

ITRS announced a new set of AI-powered SRE agents to help IT operations teams identify and resolve incidents faster and with fewer resources. 

Built on a new agentic AI layer across the ITRS real-time observability platform, the SRE agents augment IT teams with:

  • Recommendations grounded in transparent reasoning, so operators can see why an action is suggested and stay in control of decisions.
  • An agnostic inference architecture, giving clients flexibility to use their own models and benchmark performance using an in-product evaluation framework.
  • Continuous ingestion of live telemetry data (metrics, events, logs, and traces), so every recommendation is informed by real-time operational context.

”Agentic AI is becoming a cornerstone of resilience in complex, high-demanding IT environments where reliability is both business critical and regulated,” said Ryan Terpstra, CEO of ITRS. “Our SRE agents help IT operations teams spot issues earlier and get to the root cause in seconds and minutes, meaning fewer outages, faster recovery, and less client impact.”

ITRS prioritizes evidence-based reasoning and enterprise-grade controls, supporting teams responsible for complex, always-on production environments.

The initial release includes:

  • Root cause analysis (RCA) agent, correlating metrics, events, logs, and traces to surface evidence-based root-cause candidates with clear explanations.
  • Support agent, delivering natural-language, context-aware assistance across Geneos and ITRS Analytics to accelerate onboarding, configuration, and troubleshooting.
  • Website monitoring agent, automatically identifying monitoring gaps across web assets and generating the right synthetic monitors with clear, actionable recommendations to reduce mean time to identification.

“Agentic AI must work in the realities of production environments — where uptime, auditability, and accountability matter,” Terpstra continued. “Our approach enables automation that improves operational resilience without compromising compliance.”

These capabilities are available today in beta.

The Latest

While organizations want to take control of their telemetry, building telemetry pipelines from scratch can be a very daunting, complicated task, even when leveraging open-source standards like OpenTelemetry. It requires specialized knowledge across distributed systems, data engineering, and security. This fragmented approach across systems causes higher operational costs; it puts a strain on resources and reduces efficiency as teams have to work with different interfaces and processes ...

For decades, enterprise networks were designed around a simple assumption: work happened inside the office. Applications lived in centralized data centers, employees connected through internal infrastructure, and security focused on protecting the perimeter that surrounded everything ... But the way organizations operate today bears little resemblance to that environment. Cloud platforms host critical applications, employees connect from homes and airports as often as they do from offices, and partners collaborate through shared systems that exist far beyond corporate walls. In short, the corporate network no longer resembles the environment it was designed to protect ...

As an analyst who researches how IT organizations design, build, and operate their networks, I find that network data is a constant source of pain. Network teams struggle with data quality, fragmentation, authority, access, and trust. And these issues undermine everything they try to do. Here are the numbers: Only 45% of network teams are completely confident in the accuracy of their network source of truth, which documents the intent of their network ...

The 2026 Global Data Center Survey from Uptime Institute reveals an industry navigating workforce constraints, escalating outage expenses, even as rising costs remain the top concern for management teams ...

The next observability gap may not be in the code. It may be under the rack. That sounds strange until you think about how AI incidents actually feel in the middle of an investigation ... The application dashboard may be accurate. It may also be stopping at the wrong boundary. AI systems depend on software, but they also depend on a dense physical stack: racks, power paths, thermal margin, maintenance activity and, in many environments, liquid cooling. Those physical dependencies can change slowly before they look like a software incident ...

Certificate expiration is the rare outage you can see coming. Every TLS certificate carries the date it stops working, so the moment it will begin breaking connections is knowable in advance. That's what makes an expired certificate such a frustrating way to lose a service. What's changing now is how often that date comes around ...

Enterprises operate different combinations of workloads across cloud, hybrid and multicloud environments. For business-critical workloads, teams need to consider monitoring and observability early so they can detect health issues, investigate failures, and understand operational impact. Organizations place workloads on cloud platforms based on a combination of technical requirements, economics, existing dependencies, organizational standards, and business priorities. Their monitoring priorities therefore depend on what they operate and where those systems run. Those priorities will not look the same for every organization ...

Top-performing businesses prioritize data-driven decision making, enabling leaders to move from intuition and gut feel towards evidence-based judgment. But that judgment is only sound when the data underpinning decisions is accurate. With incident management, data accuracy is particularly important. Long-term revenue, customer trust, and operational stability depend on high-quality data that enables teams to quickly identify and address the root cause of major incidents. Against this backdrop, governance becomes a critical endeavor to ensure the right data drives the right action ...

In MEAN TIME TO INSIGHT Episode 26, Shamus McGillicuddy, VP of Research, Network Infrastructure and Operations, at EMA discusses network compliance ... 

Most production autonomous agents do not run in a vacuum. They run inside cloud infrastructure: virtual machines, containers, pods, managed clusters or private servers. That is where most operations teams start monitoring. Is the VM alive? Is the container running? Did the pod restart? Is memory stable? Is CPU too high? Did the health check pass? Those signals are useful. They tell you whether the shell around the agent is alive. They do not tell you whether the agent inside is actually operational ...

ITRS Introduces AI-Powered SRE Agents

ITRS announced a new set of AI-powered SRE agents to help IT operations teams identify and resolve incidents faster and with fewer resources. 

Built on a new agentic AI layer across the ITRS real-time observability platform, the SRE agents augment IT teams with:

  • Recommendations grounded in transparent reasoning, so operators can see why an action is suggested and stay in control of decisions.
  • An agnostic inference architecture, giving clients flexibility to use their own models and benchmark performance using an in-product evaluation framework.
  • Continuous ingestion of live telemetry data (metrics, events, logs, and traces), so every recommendation is informed by real-time operational context.

”Agentic AI is becoming a cornerstone of resilience in complex, high-demanding IT environments where reliability is both business critical and regulated,” said Ryan Terpstra, CEO of ITRS. “Our SRE agents help IT operations teams spot issues earlier and get to the root cause in seconds and minutes, meaning fewer outages, faster recovery, and less client impact.”

ITRS prioritizes evidence-based reasoning and enterprise-grade controls, supporting teams responsible for complex, always-on production environments.

The initial release includes:

  • Root cause analysis (RCA) agent, correlating metrics, events, logs, and traces to surface evidence-based root-cause candidates with clear explanations.
  • Support agent, delivering natural-language, context-aware assistance across Geneos and ITRS Analytics to accelerate onboarding, configuration, and troubleshooting.
  • Website monitoring agent, automatically identifying monitoring gaps across web assets and generating the right synthetic monitors with clear, actionable recommendations to reduce mean time to identification.

“Agentic AI must work in the realities of production environments — where uptime, auditability, and accountability matter,” Terpstra continued. “Our approach enables automation that improves operational resilience without compromising compliance.”

These capabilities are available today in beta.

The Latest

While organizations want to take control of their telemetry, building telemetry pipelines from scratch can be a very daunting, complicated task, even when leveraging open-source standards like OpenTelemetry. It requires specialized knowledge across distributed systems, data engineering, and security. This fragmented approach across systems causes higher operational costs; it puts a strain on resources and reduces efficiency as teams have to work with different interfaces and processes ...

For decades, enterprise networks were designed around a simple assumption: work happened inside the office. Applications lived in centralized data centers, employees connected through internal infrastructure, and security focused on protecting the perimeter that surrounded everything ... But the way organizations operate today bears little resemblance to that environment. Cloud platforms host critical applications, employees connect from homes and airports as often as they do from offices, and partners collaborate through shared systems that exist far beyond corporate walls. In short, the corporate network no longer resembles the environment it was designed to protect ...

As an analyst who researches how IT organizations design, build, and operate their networks, I find that network data is a constant source of pain. Network teams struggle with data quality, fragmentation, authority, access, and trust. And these issues undermine everything they try to do. Here are the numbers: Only 45% of network teams are completely confident in the accuracy of their network source of truth, which documents the intent of their network ...

The 2026 Global Data Center Survey from Uptime Institute reveals an industry navigating workforce constraints, escalating outage expenses, even as rising costs remain the top concern for management teams ...

The next observability gap may not be in the code. It may be under the rack. That sounds strange until you think about how AI incidents actually feel in the middle of an investigation ... The application dashboard may be accurate. It may also be stopping at the wrong boundary. AI systems depend on software, but they also depend on a dense physical stack: racks, power paths, thermal margin, maintenance activity and, in many environments, liquid cooling. Those physical dependencies can change slowly before they look like a software incident ...

Certificate expiration is the rare outage you can see coming. Every TLS certificate carries the date it stops working, so the moment it will begin breaking connections is knowable in advance. That's what makes an expired certificate such a frustrating way to lose a service. What's changing now is how often that date comes around ...

Enterprises operate different combinations of workloads across cloud, hybrid and multicloud environments. For business-critical workloads, teams need to consider monitoring and observability early so they can detect health issues, investigate failures, and understand operational impact. Organizations place workloads on cloud platforms based on a combination of technical requirements, economics, existing dependencies, organizational standards, and business priorities. Their monitoring priorities therefore depend on what they operate and where those systems run. Those priorities will not look the same for every organization ...

Top-performing businesses prioritize data-driven decision making, enabling leaders to move from intuition and gut feel towards evidence-based judgment. But that judgment is only sound when the data underpinning decisions is accurate. With incident management, data accuracy is particularly important. Long-term revenue, customer trust, and operational stability depend on high-quality data that enables teams to quickly identify and address the root cause of major incidents. Against this backdrop, governance becomes a critical endeavor to ensure the right data drives the right action ...

In MEAN TIME TO INSIGHT Episode 26, Shamus McGillicuddy, VP of Research, Network Infrastructure and Operations, at EMA discusses network compliance ... 

Most production autonomous agents do not run in a vacuum. They run inside cloud infrastructure: virtual machines, containers, pods, managed clusters or private servers. That is where most operations teams start monitoring. Is the VM alive? Is the container running? Did the pod restart? Is memory stable? Is CPU too high? Did the health check pass? Those signals are useful. They tell you whether the shell around the agent is alive. They do not tell you whether the agent inside is actually operational ...