Klaudia: Cloud-Native AI SRE

Overview

Klaudia is Komodor’s AI SRE: a family of proactive AI agents that autonomously detect, investigate, and remediate incidents across your cloud-native environment.

Kubernetes is often where failures surface, but the root cause can live across the systems around it: GitOps and delivery pipelines, networking and security layers, compute and capacity, data and messaging services, workflow engines, ML platforms, or Kubernetes control-plane configuration.

Instead of forcing engineers to jump between tools, Klaudia connects the dots in one investigation thread and provides the root cause and remediation path.

Every incident runs through a continuous loop:

Behind every investigation, a library of System Specialist Agents — each an expert in a specific platform (Argo, Postgres, Kafka, Istio, NVIDIA GPUs, vLLM, and more) — join based on where the root cause leads.

Key Capabilities

AI-Powered Investigation

• AI-Driven RCA — Klaudia conducts independent investigations automatically, correlating events, logs, configuration history, and cross-cluster signals to isolate root cause. Operational from day one, no setup required.
• Full-Stack Coverage — Klaudia connects the dots across the entire stack: a CrashLoop in Kubernetes, a slow Postgres query, a Git config change from 18 minutes ago, traffic routing, connectivity issues, and dependent services like Redis or Kafka — all within a single automated investigation. Not K8s-only. Not application-only. Klaudia follows the root cause wherever it lives and brings the full context together in one investigation thread.
• Specialized Agent Library — Behind every investigation, domain-expert agents join based on where the root cause leads — cloud, networking, data services, GitOps, ML workloads, and more.
• Clear Reasoning & Audit Trail — Every investigation step is traceable: from the triggering signal, through the evidence chain, to the remediation decision. Every action is logged.
• You can also share investigations with colleagues as well as ask Klaudia follow up questions that you may have, including across other services!

Autonomous Remediation

• One-Click Remediation — Execute fixes directly from Klaudia's RCA output, with RBAC enforcement and a full audit trail.
• Git PR Generation — For configuration-level root causes, Klaudia opens a Pull Request with the correct fix in your repository. The fix goes through your team's standard review and approval flow.
• Policy-Driven Guardrails — Control what Klaudia can act on, the scope of execution, and autonomy levels to align with your security and organizational policies.

Organizational Context

• Klaudia.md — Your architectural blueprint. Captures service dependencies, hard constraints, and topology rules — automatically loaded into every investigation to keep remediations grounded in your environment.
• Knowledge Base — Index your runbooks and postmortems. Klaudia retrieves the relevant one per incident, applying your team's specific procedures rather than generic guidance.
• Klaudia Memory — Retains what happened, what was tried, and what resolved it. Similar incidents are recognized and resolved faster with every occurrence.

Where Klaudia Works

Klaudia meets engineers where they already operate:

• Komodor UI — Persistent investigation pane across all platform views
• Slack — Trigger investigations, ask follow-ups, and approve remediations from your incident channels
• REST API — Programmatically trigger investigations and retrieve results from CI/CD pipelines, alerting platforms, or ticketing systems
• MCP Server — Expose Klaudia to external AI agents, IDEs, and internal tools via a standard protocol

Benefits

• Reduced MTTR — Replaces the manual pivot across disconnected tools with a single automated investigation thread, from alert to root cause to fix — across every layer of your cloud-native stack.
• Full-stack visibility without full-stack headcount — Klaudia investigates at the same scope as an experienced SRE: Kubernetes core, compute and capacity, networking and security, data and messaging services, GitOps delivery pipelines, and ML workloads — without requiring deep expertise in every domain on every on-call rotation.
• Fixes that address root cause, not symptoms — Remediation targets the actual failure wherever it lives: a misconfigured autoscaler blocking node provisioning, an expired certificate breaking ingress, a stalled Argo workflow corrupting a deploy, or a Kafka consumer lag taking down a downstream service — not just the pod that surfaced it. Provides clear, step-by-step remediation instructions.
• Gets faster with every incident — Klaudia's memory retains what happened and what resolved it. Recurring incidents across any domain are recognized and resolved faster over time, without re-investigating from scratch.

Security 

At Komodor, our mission is to provide unparalleled insights and capabilities around the Kubernetes stack. To achieve this, we are utilizing AWS Bedrock, as it offers the most secure and compliance-aware GenAI models available. This ensures that our customers' data remains private and protected while benefiting from cutting-edge AI-driven solutions.

• No Data Training: Komodor has opted not to allow AWS Bedrock to use any of our customer data to train its models. This guarantees that all customer data processed through the AWS Bedrock API for LLM applications is used solely for the intended purpose and not repurposed for model improvement or other uses.
• Strict Compliance and Security Measures: In line with AWS Bedrock’s commitment to high standards of data protection, they adhere to robust security measures, including SOC 2 Type 2 compliance, GDPR, CCPA, and HIPAA. Komodor aligns with these standards to ensure the highest level of data privacy and security for our customers. AWS Bedrock emphasizes, "We implement rigorous security controls and compliance measures to safeguard customer data."
• Clear Privacy Policies: AWS Bedrock provides transparency in its data privacy practices. Komodor respects and follows these guidelines, ensuring that our use of the AWS Bedrock API for LLM applications aligns with our commitment to protecting customer data and privacy.
• Data Segregation: We implement strict data isolation measures to ensure that each customer's data is securely segregated. This means that information from one customer's environment is never mixed with or accessible to another customer. 

For more detailed information, you can refer to the sources:

• AWS Bedrock Platform Security and Compliance (AWS, 2024).
• AWS Bedrock FAQ 

This approach reflects Komodor's dedication to maintaining the confidentiality and integrity of customer data while leveraging advanced AI capabilities for enhanced LLM applications.

Getting Started

Klaudia is seamlessly integrated into your Komodor experience. When investigating issues, you'll automatically see Klaudia's insights and recommendations alongside traditional metrics and logs.