🎯 Dynatrace AI Observability Workshop
Master AI/LLM monitoring with Dynatrace and the Model Context Protocol (MCP) in this hands-on workshop
📋 Workshop Overview
| Duration | 1.5 - 2 hours |
| Level | Intermediate |
| Prerequisites | GitHub account, basic Python knowledge |
🎓 What You’ll Learn
By the end of this workshop, you will be able to:
| Skill | Description |
|---|---|
| 🔬 Instrument AI Applications | Add OpenLLMetry/Traceloop to Python AI apps |
| 📊 Visualize LLM Traces | See prompts, completions, and token usage |
| 🔍 Analyze RAG Pipelines | Debug with distributed tracing |
| 🤖 Use Dynatrace MCP | Query observability data from your IDE |
| ⚡ Automate Workflows | Build AI cost alerts and daily summaries |
🗂️ Workshop Agenda
| Time | Lab | Description |
|---|---|---|
| 15 min | Lab 0: Environment Setup | Configure your GitHub Codespace |
| 15 min | Lab 1: AI Instrumentation | Add OpenLLMetry to the sample app |
| 30 min | Lab 2: Explore Traces | Analyze AI traces in Dynatrace |
| 30 min | Lab 3: Dynatrace MCP | Use MCP for agentic AI |
| 30 min | Lab 4: Workflow Automation | Automate AI cost monitoring |
🛠️ What’s Included
📦
Everything You Need
- ✅ Pre-configured GitHub Codespace with all dependencies
- ✅ Sample RAG/LLM application ready for instrumentation
- ✅ Access to Dynatrace playground environment
- ✅ Step-by-step lab guides (you're reading them!)
🚀 Ready to Begin?
Click the button below to launch your workshop environment:
Note: Each attendee gets their own isolated Codespace environment. Your changes stay local to your Codespace and won’t affect other attendees.
📞 Need Help?
- 🙋 Raise your hand in the workshop
- 📚 Check the Resources page for documentation links
- 👨🏫 Ask your workshop instructor