https://meshery-extensions.github.io/tcslabs-academy/pr-preview/pr-36/tags/observability/Recent content in Observability on TCS Labs AcademyHugoenhttps://meshery-extensions.github.io/tcslabs-academy/pr-preview/pr-36/learning-paths/deea6061-b6be-49a9-ad1c-f1a5c32e1fa9/coding-agents-for-cloud-native-operations/ai-assisted-observability-and-diagnostics/Mon, 01 Jan 0001 00:00:00 +0000https://meshery-extensions.github.io/tcslabs-academy/pr-preview/pr-36/learning-paths/deea6061-b6be-49a9-ad1c-f1a5c32e1fa9/coding-agents-for-cloud-native-operations/ai-assisted-observability-and-diagnostics/<p>Modern cloud native systems produce more signal than any engineer can manually process: MeshSync continuously reconciles cluster state, Kubernetes events stream component lifecycle changes, and Prometheus scrapes hundreds of metrics every few seconds. The challenge is no longer collecting data - it is making sense of it quickly enough to matter.</p> <p>This course shows you how to pair Meshery&rsquo;s observability layer with a coding agent to close that gap. You will learn which signals Meshery surfaces and why they are well-suited to LLM analysis, how to feed logs and events to an agent in a grounded, context-bounded way, how to construct diagnostic prompts that yield ranked hypotheses rather than speculative fixes, and how to follow a disciplined symptom-to-hypothesis workflow that keeps the agent useful without letting it run ahead of the evidence.</p>https://meshery-extensions.github.io/tcslabs-academy/pr-preview/pr-36/learning-paths/deea6061-b6be-49a9-ad1c-f1a5c32e1fa9/coding-agents-for-cloud-native-operations/ai-assisted-observability-and-diagnostics/observe/Mon, 01 Jan 0001 00:00:00 +0000https://meshery-extensions.github.io/tcslabs-academy/pr-preview/pr-36/learning-paths/deea6061-b6be-49a9-ad1c-f1a5c32e1fa9/coding-agents-for-cloud-native-operations/ai-assisted-observability-and-diagnostics/observe/