Highly Accurate, Always on Troubleshooting

Komodor doesn’t just troubleshoot fast, it troubleshoots with the specific context of your organization to get sharper with every incident it resolves. Three sources power this: knowledgebase integrations, Klaudia.md files (system blueprints capturing your topology, operational constraints, and requirements), and Klaudia Memory (memory assets built from your organization’s own investigation history). The result: faster time to root cause, less operational noise, and tribal knowledge that survives team turnover.

When self-healing is enabled, Komodor automatically detects, troubleshoots, and remediates incidents – ensuring continuous reliability so teams can focus on innovation instead of firefighting. Remediation agents can automatically generate a PR with the fix or execute safe, policy-driven actions like restarting workloads, reverting bad configs, draining unhealthy nodes, or rolling back failed releases. Teams can apply a human-in-the-loop workflow to review and approve any action before they run. Every automated action is logged, auditable, and compliant with built-in policy guardrails, so speed never comes at the cost of safety. Once an issue is resolved, Klaudia automatically validates the fix to confirm system stability.

Komodor turns troubleshooting into a collaborative experience, allowing teams to ask follow-up questions and get deeper context for any incident. Ask questions like “Why is this pod stuck in crashloop?”, “Why can’t I authenticate to the payments database”, or “Which deployment triggered this CPU spike?”, and Klaudia Chat Agent will analyze your data, trace dependencies, and respond with a clear, structured explanation, enabling even non-experts to better understand complex issues.

Komodor continuously analyzes configurations, patterns, and behavioral signals across the entire stack, to recognize emerging risks before they lead to outages. It automatically detects early indicators of instability, such as throttling, frequent restarts, resource pressure, or poor database configurations, and connects them to their underlying causes, whether in code, infrastructure, or configuration.