Komodor Expands AI SRE Platform with Klaudia Memory for Faster, More Precise Incident Resolution
Klaudia Agentic AI unifies prior investigations, customer-approved runbooks, and architecture blueprints to operationalize institutional knowledge without training on customer data
TEL AVIV, Israel and SAN FRANCISCO, July 21, 2026 (GLOBE NEWSWIRE) -- Komodor, the autonomous AI SRE company, today announced “Klaudia Memory”, a new capability that applies customer-specific knowledge from past investigations to deliver faster, more accurate root cause analysis and resolution.
As cloud native infrastructure grows in complexity, SRE teams must derive context from prior investigations, runbooks, architecture decisions, compliance rules, and institutional knowledge to resolve each new issue. The latest version of Klaudia introduces Klaudia Memory, which complements the existing Komodor Knowledge Base integration and Klaudia.md. Together, these three layers continuously deepen Klaudia's understanding of customer environments, making investigations more grounded in operational context while significantly improving both accuracy and speed.
“Since every organization has its own architecture, dependencies, constraints, and history of how past incidents were resolved, generic AI co-pilots lack the context to automate the management of enterprise production environments,” said Itiel Shwartz, Co-Founder and CTO of Komodor. “Klaudia Memory gives teams a way to preserve that institutional knowledge and make it available during every investigation, without training on customer data or exposing one customer’s information to another.”
Investigation History Compounds AI Intelligence
Klaudia Memory autonomously learns, without user intervention, from prior investigations and retains context across root cause analyses, allowing every incident to accelerate the resolution of future issues. These include failure correlations, such as “when X times out, it is usually Y”; resolution playbooks, such as “rollback resolved this issue; restart did not”; topology shortcuts, such as “this pod’s issues trace back to that certificate”; and temporal patterns, such as “OOMs every Monday after the batch job.”
Klaudia can skip paths it has already walked and begin with the likely answer already in hand. It also improves accuracy over time since Klaudia gains a deeper understanding of the customer’s environment, dependencies, and failure modes.
Klaudia Memory also helps preserve tribal knowledge, since recurring failure modes and proven fixes that often live only in senior SREs’ heads become part of the platform and remain available across clusters and teams. Over time, Klaudia also learns which signals are benign in a specific environment and stops chasing noise that does not matter.
Operationalizing Corporate Best Practices
With Knowledge Base integration, Klaudia uses customer-supplied runbooks, postmortems, and operational documents to follow approved procedures rather than generic troubleshooting steps.
During an investigation, Klaudia identifies relevant sections based on the specific failure pattern, follows established troubleshooting procedures, and uses internal terminology.
Capturing Invisible Architecture Context
To prevent applying bad fixes to production issues, Klaudia.md captures dependencies, constraints, and rules that manifests can't show, including failover policies, scaling limits driven by cost or licensing, compliance requirements, and more.
For example, Klaudia.md can tell Klaudia not to reduce a machine learning service below 16Gi of memory because it will OOMKill at startup, even at zero traffic. It serves as a reference point that is loaded at the beginning of each session to provide an always-present context on how the system works and which rules should govern remediations.
Headless Klaudia Provides Anywhere Access
The new release also expands Klaudia beyond the Komodor UI, allowing engineers and developers to trigger investigations and interact with Klaudia from the tools where they already work. Teams can call Klaudia from Slack and Microsoft Teams war rooms, developer environments such as VS Code, Claude Code, and Cursor, and existing version control and GitOps workflows. Additional API and MCP integrations make Klaudia extensible across internal tools and custom workflows, while expanded automation options support one-click remediation, self-healing policies, pull requests, tickets, and postmortems.
Finally, Komodor is constantly growing the library of specialist agents that provide investigation and remediation across the Cloud-Native ecosystem, from code to infrastructure to applications, including workloads that run outside of Kubernetes, like EC2 and ECS.
Availability
The new version of the Komodor platform is available immediately from Komodor and its business partners worldwide.
About Komodor
Komodor is the leading Autonomous AI SRE (Site Reliability Engineering) Platform for cloud-native applications. Enterprises rely on Komodor’s Agentic AI-driven autonomous self-healing and cost optimization capabilities to slash MTTR, reduce TicketOps, proactively prevent incidents, and cut cloud spend. Trusted by Fortune 500 companies across financial services, healthcare, retail, and more, Komodor eliminates operational complexity while improving application performance and resilience. The company has raised $90M in venture funding from leading investors in the US and EMEA. For more information, visit komodor.com, and follow us on LinkedIn and X.
Media Contact:
Marc Gendron
Marc Gendron PR for Komodor
marc@mgpr.net
617-877-7480
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