Argues that LLMs are only useful for DevOps and Kubernetes troubleshooting when given real operational context—CI/CD metadata, live metrics, and Kubernetes events—rather than acting as generic chatbots. Proposes a Human-in-the-loop (HITL) framework where AI proposes changes, policy-as-code tools like OPA or Kyverno validate them, and engineers give final approval before execution. Positions Devtron's platform as the governed, integrated layer that unifies these data sources so AI agents can move from reactive troubleshooting to predictive operations, such as flagging memory leak trends before they cause outages.
Table of contents
Beyond the Chatbot: The Context GapThe Risk of "Black Box" InfrastructureOperationalizing LLMs with GitOpsThe Future: From Prompting to PredictingFrom Reactive Fixes to Predictive OperationsGovernance as an Enabler, Not a BlockerConclusion146 Impressions