Build solution
Run the Full Pipeline
One command builds the solution including data processing and agent creation:
Fabric Workspace Mode
Note: Press Enter key to start or Ctrl+C to cancel the process.
Azure Only Mode (if you ran azd env set AZURE_ENV_ONLY true before deploying)
Note: Press Enter key to start or Ctrl+C to cancel the process.
This uses the data/default folder and runs all setup steps:
| Step | What Happens | Time |
|---|---|---|
| 02 | Setup Fabric workspace | ~30s |
| 03 | Load data into Fabric | ~1min |
| 04 | Generate NL2SQL prompt | ~5s |
| 05 | Create Fabric Data Agent | ~30s |
| 06 | Upload documents to AI Search | ~1min |
| 07a | Create Orchestrator Agent | ~10s |
Expected Output
> [02] Create Fabric Items... [OK]
> [03] Load Data into Fabric... [OK]
> [04] Generate Agent Prompt... [OK]
> [05] Create Fabric Data Agent... [OK]
> [06] Upload to AI Search... [OK]
> [07] Create Foundry Agent... [OK]
------------------------------------------------------------
[OK] Pipeline completed successfully!
Next: python scripts/08_test_agent.py
Test the Fabric Data Agent
- Go to your Microsoft Fabric workspace
- Select "New item"
- Search for and select "Data Agent"
- Select add data source and select your Ontology resource created in the previous step.
- Select Agent instructions and paste the below instructions.
Test the Agent
Sample Conversation
============================================================
AI Agent Chat (Fabric SQL + Native Search)
============================================================
Chat Agent: dauypdob4c4d2k-ChatAgent
SQL Mode: Fabric Lakehouse
Lakehouse: <workshop_lakehouse_1>
Type 'quit' to exit, 'help' for sample questions
------------------------------------------------------------
You: How many tickets have priority = 'High'?
Agent: There are **4 tickets** with priority **High**.
You: What is the average score from inspections?
Agent: The average score from inspections is **77**.
You: What are the requirements for handling customer tickets?
Agent: Here are the requirements for handling customer tickets:
1. **Ticket Handling Procedures**:
- All customer tickets must be acknowledged within **1 hour**.
- Closed tickets should provide feedback to clients.
2. **Priority Definitions**:
- High priority tickets must be resolved within **4 hours**.
- Medium priority tickets should be resolved within **24 hours**.
- Low priority tickets should not exceed **72 hours** for resolution.
3. **Compliance and Service Level Agreements (SLAs)**:
- Define minimum service quality levels.
- For major outages, resolutions must be completed within **4 hours**.
4. **Documentation Requirements**:
- All outages and tickets must be documented, including resolution times and customer impacts.
5. **Response Time Expectations**:
- Representatives must respond to outage inquiries within **1 hour** during business hours and within **3 hours** outside of business hours.
6. **Ticket Escalation Process**:
- All outages and tickets must be documented, including resolution times and customer impacts.
7. **Customer Feedback Mechanism**:
- Gather customer feedback through follow-up surveys sent within one week of ticket closure.
These procedures ensure timely communication, accountability, and continuous improvement in customer service.
You: Are we meeting our resolution targets for high priority tickets according to our Ticket Management Policy?
Agent: According to the Ticket Management Policy, high priority tickets must be resolved within 4 hours. Here’s the current status regarding high priority tickets:
- **Total High Priority Tickets:** 4
- **High Priority Tickets Resolved Within Target (4 hours):** 1
This indicates that we are currently not meeting our resolution target for high priority tickets, as only 1 out of 4 tickets were resolved within the specified timeframe.
You: quit
Checkpoint
Solution Deployed!
You now have a working solution with:
- Data queries via Fabric IQ or Azure SQL function tools for AzureOnly mode
- Foundry IQ retrieving document knowledge
- Orchestrator Agent combining both sources