… Why Standard Monitoring Falls Short Standard application monitoring answers questions like: Is the service up? How fast are responses? Are there errors? Agent monitoring needs to answer different questions: Why did this task cost dramatically more than usual? …
… LLMOps, or large language model operations, is the set of practices, tools, and workflows used to develop, deploy, and manage LLMs throughout their production lifecycle: data management, prompt engineering and fine-tuning, deployment and serving, monitoring and evaluation, and security and governan… …
… Monitoring, backup capabilities, lifecycle management and observability are still key concerns. …
… In many organizations, every team answers these questions independently. …
… Two modes: Monitor and Guardrails Monitor mode evaluates every tool call against your rules and logs the results, but does not enforce any action. …
… OpenTelemetry Integration — Adding end-to-end observability across the SDK with structured logging and distributed tracing, so platform teams can monitor and debug ML workloads with the same tooling they use for the rest of their infrastructure. …
… A single kernel exploit not only breaches every container, it simultaneously blinds every monitor watching it. …
… The two questions I keep getting are why cloud native at all for agentic AI, and where the human and the team sit in the loop. …
… Additionally, proxy-based enforcement introduces another operational component that must be secured and monitored. …
… If you want to explore k0rdent further, dig into the project on GitHub and bring your questions or feedback to the k0rdent channel on the CNCF Slack . …