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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.

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I've spent a lot of time in the channel, and one thing I keep coming back to is this: a partner program is only as good as what it looks like in the field. Many programs look great on paper, but when a partner is in front of a customer navigating a complex hybrid environment or trying to make the case for AI-powered observability, the gap between what a vendor promises and what it actually delivers becomes very clear, very fast ...

Enterprises today operate in a real-time environment where uninterrupted access to trusted data has become a baseline expectation for users, applications and automated systems. Traditional DataOps models, built on manual effort and human triage, cannot keep pace with this always active demand. AI agents are emerging as the operational backbone, ensuring consistent data availability, reinforcing trustworthiness and enabling a level of scale that manual processes cannot achieve ...

For decades, trust in the digital workplace rested on familiar signals. We trusted faces on video calls, voices on the phone, and emails that appeared to come from people we knew. These cues felt human and intuitive. They anchored how decisions were made, approvals were granted, and access was authorized. AI-powered deepfakes have quietly broken that model ...

Cloud migration was supposed to be a one-way door. For most enterprises, it turns out it isn't. Cloud data repatriation is a real and growing trend. A new survey ... finds that 89% of organizations plan to expand their on-premises infrastructure footprint over the next two years — and 75% have already moved at least some workloads back from public cloud in the past 24 months. The findings point to a broad rethinking of where data belongs ...

Over the past few years, large language models (LLMs) have revolutionized the software industry. Given their ability to excel at multi-step reasoning, LLMs have helped enterprises streamline workflows and adapt to the unknown. However, employing such models comes with sky-high costs, latency issues, and limited flexibility. In the realm of IT operations, it is generally wiser to employ smaller, domain-specific models instead ...

For years, DevOps teams operated under a simple assumption: collect enough telemetry, and you can find and fix any problem. That assumption is breaking down. Modern enterprises now operate across microservices, hybrid cloud environments, APIs, Kubernetes, and highly automated delivery pipelines. Releases happen continuously, dependencies shift constantly, and failures spread faster than teams can diagnose them ...

New Relic surveyed IT and engineering leaders from the media and entertainment (M&E) sector to understand what's working — and where challenges persist with their observability practices. The findings reveal how M&E organizations are navigating rising platform complexity, audience expectations, and AI-driven change. Below are five takeaways that stand out ...

Let me start with something I've seen play out more times than I can count. A team hits a wall with the cloud. Costs creep up, then spike. Performance starts to feel inconsistent. Someone in finance asks a simple question like "why did this double?" and nobody has a clean answer ... Maybe this isn't the right place for everything. That realization feels like a breakthrough, like you've identified the problem. In reality, you've just identified the starting line ...

In MEAN TIME TO INSIGHT Episode 24, Shamus McGillicuddy, VP of Research, Network Infrastructure and Operations, at EMA discusses network observability tool sprawl ... 

In cloud-native systems, scaling is often as simple as moving a slider. For on-premise databases, the stakes are different. Over-provisioning hardware is expensive. Under-provisioning leads to performance bottlenecks that are difficult to fix once the equipment is in the rack ...