From 31667451232b1f6596b787dbadff7cdb1c8daa39 Mon Sep 17 00:00:00 2001
From: Nubenetes Bot <bot@nubenetes.com>
Date: Thu, 28 May 2026 20:22:55 +0200
Subject: [PATCH] feat: add Grafana AI Observability video to V2 Video Hub

---
 data/inventory.yaml         | 21 +++++++++++++++++++++
 v2-docs/videos/ai-agents.md | 23 ++++++++++++++++++-----
 2 files changed, 39 insertions(+), 5 deletions(-)

diff --git a/data/inventory.yaml b/data/inventory.yaml
index e784574e..aedf171e 100644
--- a/data/inventory.yaml
+++ b/data/inventory.yaml
@@ -308035,3 +308035,24 @@ https://www.youtube.com/@arsys:
     order_v1: 133
     order_v2: 14
   addition_method: manual
+https://www.youtube.com/embed/HI6KleJAZPY:
+  title: I Stopped Staring at Dashboards. AI Reads My Grafana Metrics Now.
+  ai_summary: This video explores how AI agents can read Grafana metrics, logs, and
+    traces directly using Grafana Assistant and Claude Code wired up to the Grafana
+    MCP server. It demonstrates querying Prometheus, Loki, and Tempo, generating custom
+    dashboards from natural language, and analyzing runtime data from the command
+    line. This workflow bridges the gap between analysis and remediation, allowing
+    agents to verify fixes and make data-grounded decisions.
+  category: Observability and Monitoring
+  technology: Grafana Assistant, Claude Code, Grafana MCP Server
+  description: Featured video in the Top Videos & Clips section.
+  health_score: 100.0
+  is_enriched: true
+  is_featured_video: true
+  last_checked: 0.0
+  stars: 0
+  status: online
+  v1_locations: []
+  video_order: 20
+  year: '2026'
+  addition_method: manual
diff --git a/v2-docs/videos/ai-agents.md b/v2-docs/videos/ai-agents.md
index 0748c6bd..35ae0386 100644
--- a/v2-docs/videos/ai-agents.md
+++ b/v2-docs/videos/ai-agents.md
@@ -6,11 +6,12 @@ Welcome to the **AI Agents and MCP** section of the V2 Video Hub. Explore curate
 
 1. [Red Hat OpenShift](#red-hat-openshift)
 2. [Generative AI and Large Language Models](#generative-ai-and-large-language-models)
-3. [Red Hat OpenShift AI](#red-hat-openshift-ai)
-4. [Claude Code](#claude-code)
-5. [Neural Networks](#neural-networks)
-6. [LLM Architecture and Post-Training](#llm-architecture-and-post-training)
-7. [Agentic DevOps](#agentic-devops)
+3. [Grafana Assistant Claude Code Grafana MCP Server](#grafana-assistant-claude-code-grafana-mcp-server)
+4. [Red Hat OpenShift AI](#red-hat-openshift-ai)
+5. [Claude Code](#claude-code)
+6. [Neural Networks](#neural-networks)
+7. [LLM Architecture and Post-Training](#llm-architecture-and-post-training)
+8. [Agentic DevOps](#agentic-devops)
 
 ## Red Hat OpenShift
 
@@ -36,6 +37,18 @@ Welcome to the **AI Agents and MCP** section of the V2 Video Hub. Explore curate
 
     </center>
 
+## Grafana Assistant Claude Code Grafana MCP Server
+
+??? note "🎬 I Stopped Staring at Dashboards. AI Reads My Grafana Metrics Now."
+    !!! info "Architectural Summary"
+        This video explores how AI agents can read Grafana metrics, logs, and traces directly using Grafana Assistant and Claude Code wired up to the Grafana MCP server. It demonstrates querying Prometheus, Loki, and Tempo, generating custom dashboards from natural language, and analyzing runtime data from the command line. This workflow bridges the gap between analysis and remediation, allowing agents to verify fixes and make data-grounded decisions.
+
+    <center markdown="1">
+
+    <iframe width="720" height="405" src="https://www.youtube.com/embed/HI6KleJAZPY" title="I Stopped Staring at Dashboards. AI Reads My Grafana Metrics Now." frameborder="0" allow="accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture" allowfullscreen loading="lazy" style="border: 1px solid var(--md-typeset-table-color); border-radius: 8px;"></iframe>
+
+    </center>
+
 ## Red Hat OpenShift AI
 
 ??? note "🎬 Red Hat OpenShift AI overview"