3 min read Komodor today announced the Komodor Agentic Operations Platform which enables organizations to rapidly and safely deploy autonomous workflows to handle escalating burdens on production teams. Using the same infrastructure and tools that underpin Komodor's enterprise-proven AI SRE platform, organizations can now build or import their own agents under shared governance and context, and orchestrate them in end-to-end workflows for AI SRE, AI Software Operations, and Cost Optimization.
10 min read The second stage of AI SRE maturity is moving the agent off your machine and pointing it at real clusters — read-only, in shadow mode — then climbing a trust ladder toward carefully scoped action.
3 min read Klaudia Agentic AI unifies prior investigations, customer-approved runbooks, and architecture blueprints to operationalize institutional knowledge.
9 min read The first stage of AI SRE maturity is a laptop, a throwaway cluster, and zero production access. Here's how to set it up, and what to watch for.
7 min read For teams with strong engineering talent, creating a DIY AI SRE seems like a straightforward challenge. But the decision to build or buy is a critical strategic choice.
4 min read Komodor partnered with a leading AI Cloud Provider to tackle their operational hurdles. Here's how our AI SRE, Klaudia, successfully bridged the visibility gaps in their highly customized CAPI infrastructure.
8 min read At KubeCon Europe 2026, Komodor is unveiling a new extensible multi-agent architecture for Klaudia AI. To understand why it matters, it helps to start with why building AI for infrastructure is so fundamentally hard.
6 min read Platform teams find themselves caught in the middle, trying to optimize shared infrastructure while both sides insist their priorities are non-negotiable. This conflict plays out across enterprises constantly, and it reveals a fundamental problem with how cost optimization works in cloud-native environments. The typical FinOps model, where a centralized team identifies savings opportunities and pushes recommendations to engineering, assumes that cost and operations are separate domains that can be optimized independently. In Kubernetes, that assumption breaks down completely.
6 min read Part 8 of our AI SRE in Practice Series. This scenario walks through how AI-augmented troubleshooting enables engineers without Kubernetes expertise to diagnose and resolve complex issues, using a real example from a team onboarding non-experts to platform operations.
