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5 changes: 4 additions & 1 deletion docs/DeploymentGuide.md
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10. If you are done trying out the application, you can delete the resources by running `azd down`.


### 🛠️ Troubleshooting
If you encounter any issues during the deployment process, please refer [troubleshooting](../docs/TroubleShootingSteps.md) document for detailed steps and solutions.

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"jinja": true
}
```
## Next Steps
Now that you've completed your deployment, you can start using the solution.

To help you get started, here are some [Sample Questions](./SampleQuestions.md) you can follow to try it out.
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# Sample Workflow

To help you get started, here are some **Sample Prompts** you can ask in the app:

## **Teams**
Select the Team option from the top-left section, then click Continue after choosing the desired team.

By default, three teams are available after running the post-deployment scripts:
- Retail
- HR
- Marketing

![Application](images/samplequestion_1.png)

### **Retail Scenario**
If you select the Retail team, follow the prompts below.

>**Agents Used:** Customer, Order, Analysis Recommendation

The Retail scenario enables users to explore and access information related to the Retail team and its associated data. Key activities include:

_Sample operation:_
- Task: From the Quick Tasks, select **"Satisfaction Plan"** and submit it.
> _Note: Average response time is 15–20 seconds._ <br>
> _Observe: An analysis of Emily Thompson’s satisfaction with Contoso has been generated. It provides a proposed plan consisting of four or more steps._

- Task: Click **"Approve Task Plan"** Button
> _Note: Average response time is 01 minute 15 seconds._ <br>
> _Observe: It goes into "Thinking Process", "Processing your plan" and "coordinating with AI Agents"._ <br>
> _Review the output._

### **Product Marketing Scenario**
If you select the Marketing team, follow the prompts below.

>**Agents Used:** Product, Marketing , Proxy

The Product Marketing Scenario allows users to explore and retrieve information related to Marketing and specific product. Key tasks include:

_Sample operation:_
- Task: Switch to **"Product Marketing Team"** from the top left section and click **"Continue"** button.
- Task: From the Quick Tasks, select **"Draft a press release"** and submit it.
> _Note: Average response time is 15–20 seconds._ <br>
> _Observe: It will trigger the "Generating Plan Action" and give the Proposed Plan with 4 or more Steps_
- Task: Click on **"Approve Task Plan"** Button
> _Note: Average response time is around 01 minute._ <br>
> _Observe: It goes into "Thinking Process" and observe a spinner "Processing your plan and coordinating with AI Agents"._ <br>
> _Review the output._

### **HR Onboarding Scenario**
If you select the HR team, follow the prompts below.

>**Agents Used:** HR Helper, Technical support , Proxy

The HR Onboarding Scenario allows users to explore and retrieve information related to OnBoarding the Employee. Key tasks include:

_Sample operation:_

- Task: Switch to the **"Human Resources Team"** from the top left section and click **"Continue"**
- Task: From the Quick Tasks, select **"Onboard New Employee"** and submit it.

> _Note: Average response time is 15–20 seconds._ <br>
> _Observe: If it asks for additional clarification (Human in the loop) Please provide this information irrespective of what specific information is asked. This will prevent agent for asking for multiple clarifications_

```sh
department: hr, role: manager, start date: 11/23/2025, orientation date: 11/25/2025, location: onsite, email: js@contoso.com, mentor: Jim Shorts, benefits package: standard, ID Card: yes, salary: 70000, Laptop : Dell 14 Plus
```
> _Observe: It will trigger "Generating Plan Action" and "Proposed Plan" with 4 or more Steps_
</br>

- Task: Click on **"Approve Task Plan"** Button.
> _Note: Average response time is around 01 minute 15 seconds._ <br>
> _Observe: It goes into "Thinking Process", "Processing your plan" and "coordinating with AI Agents"_ <br>
> _Review the output._


This structured approach ensures that users receive automated, AI-coordinated task execution and intelligent responses from specialized agents.
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from pathlib import Path

# Add the backend path to sys.path so we can import v3 modules
backend_path = Path(__file__).parent.parent.parent / "backend"

backend_path = Path(__file__).parent.parent.parent.parent / "backend"
sys.path.insert(0, str(backend_path))

from v3.magentic_agents.models.agent_models import MCPConfig, SearchConfig
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