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🎉 Lab Complete — Predictive Maintenance (TireForge Industries)

Congratulations — you've built, instrumented, evaluated, and deployed a production-ready multi-agent AI system from scratch. Here's what you accomplished.

Recap

# Challenge What You Built
0 Setup Provisioned a Microsoft Foundry Resource, project, GPT model deployment, Log Analytics workspace, and Application Insights instance via a single deploy.sh script
1 Build Agents Created an Anomaly Detection Agent (reads live sensor telemetry — temperature, vibration, pressure — and identifies machines operating outside safe thresholds) and a Fault Diagnosis Agent (determines root cause and recommends maintenance actions per machine type)
2 Monitor Enabled OpenTelemetry GenAI tracing — every model call, tool invocation, and token count is captured as a distributed trace in Application Insights
3 Evaluate Ran systematic LLM-as-judge evaluations across the full sensor dataset, producing repeatable coherence and relevance scores you can version-track across prompt changes
4 Production Workflow Wired both agents into an orchestrated pipeline in the Foundry portal — a stable, testable endpoint with run history that plant operators can inspect

Skills you practiced

  • Designing agent system prompts with clear role boundaries and constraints
  • Grounding agents in real sensor telemetry via tool calls (function calling)
  • Distributed tracing for AI systems with OpenTelemetry
  • LLM-as-judge evaluation with the Azure AI Evaluation SDK
  • Multi-agent orchestration in the Foundry portal

Next Steps

Want to take the TireForge system further? Here are some directions:

  • Add more agents — a Parts Inventory agent that checks whether replacement components are in stock before recommending maintenance, or a Scheduling agent that finds the earliest maintenance window with minimal production impact
  • Connect real data — replace the static sensor_data.json with a live IoT Hub or Azure Event Hub stream
  • Improve evaluation — add task-specific evaluators (e.g., "did the agent correctly identify a Curing Press failure from elevated temperature + abnormal pressure combination?") alongside the generic coherence scores
  • Set up CI/CD — run your evaluation dataset automatically on every prompt change using GitHub Actions and fail the build if quality scores drop below a threshold
  • Explore fine-tuning — use your traced fault diagnoses as training data to fine-tune a smaller, cheaper model for the initial anomaly detection step
  • Try another scenario — the Claims and Call Center scenarios cover insurance processing and customer support using the same lifecycle

Clean Up Azure Resources

Important: The resources deployed in Challenge 0 incur Azure costs while they exist. Delete them when you're done.

What gets deleted

  • The resource group foundry-hackathon-rg-<suffix> and everything inside it:
  • Microsoft Foundry Resource + project
  • GPT model deployment
  • Log Analytics workspace
  • Application Insights instance

Option 1 — Script

Run the cleanup script from the repo root:

bash factory/cleanup.sh

The script reads the .env file written by deploy.sh so it knows exactly which resource group to target. It asks for confirmation before deleting.

Option 2 — Azure Portal

  1. Go to portal.azure.com
  2. Search for Resource groups
  3. Find foundry-hackathon-rg-<your-suffix>
  4. Click Delete resource group and confirm

Option 3 — Azure CLI

# Replace <suffix> with the value shown in your .env file
az group delete --name foundry-hackathon-rg-<suffix> --yes --no-wait