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Komodor Introduces Autonomous Self-Healing Capabilities

Komodor announced the release of autonomous self-healing and cost optimization capabilities that simplify operations for SRE, DevOps, and Platform teams managing large-scale Kubernetes environments. 

Powered by Klaudia, purpose-built agentic AI, the Komodor platform can automatically detect, investigate, and remediate issues, with or without a human in the loop, and optimize resource utilization.

Klaudia, Komodor’s agentic AI technology, provides detection, root cause analysis, and automated remediation of issues based on deep Kubernetes expertise. Trained on telemetry from thousands of production environments, Klaudia has been proven accurate across thousands of incidents, the essential first step in any autonomous workflow. By pairing domain intelligence with trusted automation, Klaudia minimizes downtime, prevents recurring failures, and sustains reliability at scale.

Powered by Klaudia Agentic AI, the Komodor platform continuously monitors workloads, applies reasoning and causality to identify anomalies, and automatically remediates issues in alignment with enterprise policies. Key capabilities include:

  • Autonomous self-healing with a human-in-the-loop option to resolve common failures such as pod crashes, misconfigurations, and failed rollouts before they escalate into outages.
  • Active guardrails that let teams define and scope automation based on pre-defined levels to ensure actions stay within desired operational boundaries.
  • Iterative learning loops driven by continuous health checks and user feedback, enhancing the platform’s ability to detect, investigate, and remediate issues with ever-growing precision.
  • Explainable AI makes every action transparent and traceable. By explaining what happened, why it happened, how it was fixed, and the current system state, ensuring Klaudia acts as a trusted co-pilot, not a black box.

“Reliability engineering has always been reactive. With autonomous self-healing, we are flipping the script on the traditional management model so organizations can move from firefighting to proactive resilience,” said Itiel Shwartz, Co-Founder & CTO of Komodor. “Due to the accuracy of our Klaudia Agentic AI technology, we enable enterprises to keep clusters and workloads healthy, and cut operational costs, with little or no manual effort.”

Komodor’s new autonomous cost optimization capabilities provide the following advantages:

  • Dynamically right-sizing workloads to balance cost, performance, and reliability.
  • Intelligently scheduling pods to avoid bin-packing restrictions, idle resources, and unnecessary scaling.
  • Preventing reliability risks that often arise from static or overly aggressive scaling policies.
  • Using PodMotion to seamlessly move pods and its state across nodes with zero downtime, helping organizations cut costs, boost efficiency, and handle infrastructure events without disrupting applications.

Komodor’s evolution into an AI-powered SRE platform is grounded in five years of production experience supporting dozens of large enterprises, including multiple Fortune 500 organizations, running Kubernetes at scale. This deep operational history has trained the Klaudia agentic AI technology with rich, mission-specific context, enabling precise, trusted automation across detection, diagnosis, remediation, and optimization. The platform is fully enterprise-ready, with robust security and compliance capabilities such as RBAC, SSO, SAML, SCIM, audit logging, and certifications including GDPR and SOC 2 Type II.

The Komodor Platform with Autonomous AI SRE capabilities is available immediately from Komodor and its business partners worldwide.

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Komodor Introduces Autonomous Self-Healing Capabilities

Komodor announced the release of autonomous self-healing and cost optimization capabilities that simplify operations for SRE, DevOps, and Platform teams managing large-scale Kubernetes environments. 

Powered by Klaudia, purpose-built agentic AI, the Komodor platform can automatically detect, investigate, and remediate issues, with or without a human in the loop, and optimize resource utilization.

Klaudia, Komodor’s agentic AI technology, provides detection, root cause analysis, and automated remediation of issues based on deep Kubernetes expertise. Trained on telemetry from thousands of production environments, Klaudia has been proven accurate across thousands of incidents, the essential first step in any autonomous workflow. By pairing domain intelligence with trusted automation, Klaudia minimizes downtime, prevents recurring failures, and sustains reliability at scale.

Powered by Klaudia Agentic AI, the Komodor platform continuously monitors workloads, applies reasoning and causality to identify anomalies, and automatically remediates issues in alignment with enterprise policies. Key capabilities include:

  • Autonomous self-healing with a human-in-the-loop option to resolve common failures such as pod crashes, misconfigurations, and failed rollouts before they escalate into outages.
  • Active guardrails that let teams define and scope automation based on pre-defined levels to ensure actions stay within desired operational boundaries.
  • Iterative learning loops driven by continuous health checks and user feedback, enhancing the platform’s ability to detect, investigate, and remediate issues with ever-growing precision.
  • Explainable AI makes every action transparent and traceable. By explaining what happened, why it happened, how it was fixed, and the current system state, ensuring Klaudia acts as a trusted co-pilot, not a black box.

“Reliability engineering has always been reactive. With autonomous self-healing, we are flipping the script on the traditional management model so organizations can move from firefighting to proactive resilience,” said Itiel Shwartz, Co-Founder & CTO of Komodor. “Due to the accuracy of our Klaudia Agentic AI technology, we enable enterprises to keep clusters and workloads healthy, and cut operational costs, with little or no manual effort.”

Komodor’s new autonomous cost optimization capabilities provide the following advantages:

  • Dynamically right-sizing workloads to balance cost, performance, and reliability.
  • Intelligently scheduling pods to avoid bin-packing restrictions, idle resources, and unnecessary scaling.
  • Preventing reliability risks that often arise from static or overly aggressive scaling policies.
  • Using PodMotion to seamlessly move pods and its state across nodes with zero downtime, helping organizations cut costs, boost efficiency, and handle infrastructure events without disrupting applications.

Komodor’s evolution into an AI-powered SRE platform is grounded in five years of production experience supporting dozens of large enterprises, including multiple Fortune 500 organizations, running Kubernetes at scale. This deep operational history has trained the Klaudia agentic AI technology with rich, mission-specific context, enabling precise, trusted automation across detection, diagnosis, remediation, and optimization. The platform is fully enterprise-ready, with robust security and compliance capabilities such as RBAC, SSO, SAML, SCIM, audit logging, and certifications including GDPR and SOC 2 Type II.

The Komodor Platform with Autonomous AI SRE capabilities is available immediately from Komodor and its business partners worldwide.

The Latest

Pilots are everywhere, stakeholders are seeking results, businesses are pushing for new tools, and IT teams are being asked to make AI secure, reliable, and useful at scale. But as organizations move from testing AI to operationalizing it, many are discovering that the biggest barrier is not the model, the use case, or even the budget. It is the file data foundation within ...

Fast or cheap? For most of my career in engineering, speed and quality sat on opposite ends of a seesaw. The "OR" in "fast or cheap" was non-negotiable. It was expected that pushing for faster releases meant that something in quality would give way. Tightening quality controls meant the schedule slipped. Every engineering leader I know has lived some version of that tradeoff ... The seesaw is starting to level out ...

I have been building enterprise software for more than 20 years ... One thing stays true across all of it: You do not find out your foundation is wrong during the crisis. You find out when the debt comes due. For a lot of organizations, that bill is arriving now. New research ... puts hard numbers on something practitioners have been sensing for a while. The telemetry problem isn't coming. It's already here ...

The rapid growth of AI workloads is pushing traditional log management approaches to their limits, according to The State of Log Management 2026 report from Dynatrace. Modern logs have become critical to understanding, validating, and securing AI-driven decisions, helping organizations ensure reliability, compliance, and performance at scale. However, the volume and complexity of AI telemetry are overwhelming legacy tools ...

For years, secure connectivity has relied on a familiar pattern: route traffic back to centralized gateways, inspect it, and then allow access. This model worked when applications lived in a handful of data centers and users were largely confined to offices. That model is now under strain. Applications are distributed across clouds, users connect from everywhere, and real-time workloads demand performance that centralized inspection points struggle to deliver. As traffic volumes grow and latency expectations shrink, routing everything through a small number of control points has become both a performance bottleneck and a resilience risk. The future of secure connectivity requires a different approach ...

The AI experimentation phase is over, and the private cloud is where enterprise AI workloads are being deployed for security and scale, according to Private Cloud Outlook 2026, a new report from Broadcom ... 2026 marks an acceleration into a full AI tipping point. The shift is being shaped by three forces — costs, complexity, and control — that public cloud environments are increasingly failing to address for production AI at scale. Key findings from the report include ...

44% of organizations have reported an outage in the past year tied to suppressed or ignored alerts, and 78% had at least one incident where no alert was fired at all ... Engineers learned about failures from customers. That gap between what our tools report and what our customers experience is the problem DevOps teams have been quietly solving with GenAI tooling, even as most enterprises continue to run their NOCs on manual alert triage ...

Cloud outages are usually described as technical failures. When a service goes down, a dependency breaks, or a region has issues, the focus immediately shifts to infrastructure. But if you look closely at how these incidents actually unfold, the root cause is rarely the technology itself. It is almost always tied to decisions made earlier, during design, implementation, or day-to-day operations. The system behaves the way it was built. The real question is how it was built ...

77% of leaders say their teams need AI skills urgently. 64% say their organization plans to train current employees rather than hire new ones. So far, so reasonable. The part that surprised me is who's been put in charge: 34% of those leaders say IT and engineering own the AI skills mandate. Learning and Development or HR own it at 7% of organizations. That's roughly five-to-one in favor of the people who understand the tools, over the people whose actual job is teaching adults how to learn new ones ...

In the ever-evolving digital landscape, enterprises are increasingly focused on enhancing their observability stacks to gain deeper insights into their IT environments. Observability has become a cornerstone of modern IT operations, enabling organizations to monitor, diagnose, and optimize their systems with unprecedented precision. However, a critical piece of the puzzle often goes unnoticed in this transformation: IBM i ...