Update README files for clarity and consistency; add deployment notes and resource verification steps

Author: pewill-msft

callcenter/challenge-0-setup/README.md

@@ -79,20 +79,26 @@ From the **callcenter** folder, run one of the following depending on your termi
 
 > **Tip:** If you're on Windows without WSL, open the integrated VS Code terminal and switch to **Git Bash** using the dropdown next to the `+` button.
 
-This will provision all resources **and** automatically write your `.env` file to the repository root as `.env`.
+This will provision all resources **and** automatically write your `.env` file to the repository root as `.env`. The deployment will take a couple of minutes to complete.
 
 ## Verify the creation of your resources
 
-Go to the [Azure Portal](https://portal.azure.com/) and find your resource group, which should now contain resources like this (exact names will differ depending on scenario):
+Go to the [Azure Portal](https://portal.azure.com/) and find your resource group, which should now contain resources like this:
 
 ![Azure Portal Resources](./images/azure-portal-resources.png)
 
-Go to the [Microsoft Foundry Portal](https://ai.azure.com) and verify that you can access the Foundry project.
+> [!NOTE]
+> The resource name prefixes vary by scenario and the suffixes are unique for each deployment
+
+Go to the [Microsoft Foundry Portal](https://ai.azure.com/nextgen) and verify that you can access the Foundry project.
 
 ![Foundry Project](./images/foundry-project.png)
 
 Select **Build** in the top navigation, then **Models**, and verify that the **gpt-5.4** model is deployed.
 
+>[!NOTE]
+> In some versions of the Foundry Portal the **Models** tab is rebranded to **Deployments** but they serve the same purpose.
+
 ![Foundry Model](./images/foundry-model.png)
 
 Select **gpt-5.4**, enter a test message in the model playground, and verify that you get a response.

callcenter/challenge-0-setup/images/azure-portal-resources.png

@@ -73,7 +73,7 @@ Once the script finishes, your traces are live. Explore them in the Azure Portal
 
 ### Step 1: Microsoft Foundry Portal
 
-1. Go to [foundry.microsoft.com](https://ai.azure.com/nextgen) → open your project
+1. Go to [Microsoft Foundry Portal](https://ai.azure.com/nextgen) → open your project
 2. Click on the `resolution-advisor-agent` -> **Traces** 
 
    - **Traces panel** — The **Conversations** tab lists every agent run as a row, showing the conversation ID, trace ID, response ID, status, creation time, duration, tokens in/out, estimated cost, evaluation results, and agent version. Use the search box and the **Status**, **Duration**, **Tokens**, and **Estimated Cost** filters (plus the date-range selector) to narrow results, switch to the **Responses** tab for individual model responses, or click **Create dataset** to turn these traces into an evaluation dataset.

callcenter/challenge-2-monitor/README.md

@@ -71,7 +71,7 @@ The evaluation dataset has already been prepared for you as [eval_portal.jsonl](
 3. Select **Agent** as the evaluation target
 4. Choose `intent-classification-agent` from the dropdown
 5. Select **Individual Turns** and then **Existing Dataset**
-6. Click on **Upload new dataset**. **You must enter a dataset name first** — the upload stays disabled until you do. Type a name (e.g. `callcenter-eval`), then add the file located on `callcenter/challenge-3-evaluate/eval_portal.jsonl` and confirm the upload.
+6. Click on **Upload new dataset**. You must enter a dataset name first — the upload stays disabled until you do. Type a name (e.g. `callcenter-eval`), then add the file located on `callcenter/challenge-3-evaluate/eval_portal.jsonl` and confirm the upload.
 7. Leave the **Field Mapping** and **Configure Agents** fields as is.
 8. In the **Criteria** step, keep only **Coherence** and **Fluency**. Remove every other evaluator — in particular **deselect Tool Call Accuracy**, since the agents can't execute the local tools during evaluation and will always score low on it. Trimming the evaluator list also makes the run significantly faster.
 9. Leave the Evaluation Name as is or configure to your liking.

callcenter/challenge-3-evaluate/README.md

@@ -74,20 +74,26 @@ From the **claims** folder, run the deploy script:
 bash challenge-0-setup/deploy.sh
 ```
 
-This will provision all resources **and** automatically write your `.env` file to the repository root as `.env`.
+This will provision all resources **and** automatically write your `.env` file to the repository root as `.env`. The deployment will take a couple of minutes to complete.
 
 ## Verify the creation of your resources
 
-Go to the [Azure Portal](https://portal.azure.com/) and find your resource group, which should now contain resources like this (exact names will differ depending on scenario):
+Go to the [Azure Portal](https://portal.azure.com/) and find your resource group, which should now contain resources like this:
 
 ![Azure Portal Resources](./images/azure-portal-resources.png)
 
-Go to the [Microsoft Foundry Portal](https://ai.azure.com) and verify that you can access the Foundry project.
+> [!NOTE]
+> The resource name prefixes vary by scenario and the suffixes are unique for each deployment
+
+Go to the [Microsoft Foundry Portal](https://ai.azure.com/nextgen) and verify that you can access the Foundry project.
 
 ![Foundry Project](./images/foundry-project.png)
 
 Select **Build** in the top navigation, then **Models**, and verify that the **gpt-5.4** model is deployed.
 
+>[!NOTE]
+> In some versions of the Foundry Portal the **Models** tab is rebranded to **Deployments** but they serve the same purpose.
+
 ![Foundry Model](./images/foundry-model.png)
 
 Select **gpt-5.4**, enter a test message in the model playground, and verify that you get a response.

claims/challenge-0-setup/README.md

@@ -73,7 +73,7 @@ Once the script finishes, your traces are live. Explore them in the Azure Portal
 
 ### Step 1: Microsoft Foundry Portal
 
-1. Go to [foundry.microsoft.com](https://ai.azure.com/nextgen) → open your project
+1. Go to [Microsoft Foundry Portal](https://ai.azure.com/nextgen) → open your project
 2. Click on the `claims-decision-agent` -> **Traces** 
 
    - **Traces panel** — The **Conversations** tab lists every agent run as a row, showing the conversation ID, trace ID, response ID, status, creation time, duration, tokens in/out, estimated cost, evaluation results, and agent version. Use the search box and the **Status**, **Duration**, **Tokens**, and **Estimated Cost** filters (plus the date-range selector) to narrow results, switch to the **Responses** tab for individual model responses, or click **Create dataset** to turn these traces into an evaluation dataset.

claims/challenge-0-setup/images/azure-portal-resources.png

@@ -71,7 +71,8 @@ The evaluation dataset has already been prepared for you as [eval_portal.jsonl](
 3. Select **Agent** as the evaluation target
 4. Choose `claims-triage-agent` from the dropdown
 5. Select **Individual Turns** and then **Existing Dataset**
-6. Click on **Upload new dataset**. **You must enter a dataset name first** — the upload stays disabled until you do. Type a name (e.g. `claims-eval`), then add the file located on `claims/challenge-3-evaluate/eval_portal.jsonl` and confirm the upload.
+6. Click on **Upload new dataset**. 
+You must enter a dataset name first — the upload stays disabled until you do. Type a name (e.g. `claims-eval`), then add the file located on `claims/challenge-3-evaluate/eval_portal.jsonl` and confirm the upload.
 7. Leave the **Field Mapping** and **Configure Agents** fields as is.
 8. In the **Criteria** step, keep only **Coherence** and **Fluency**. Remove every other evaluator — in particular **deselect Tool Call Accuracy**, since the agents can't execute the local tools during evaluation and will always score low on it. Trimming the evaluator list also makes the run significantly faster.
 9. Leave the Evaluation Name as is or configure to your liking.

claims/challenge-2-monitor/README.md

@@ -74,20 +74,26 @@ From the **factory** folder, run the deploy script:
 bash challenge-0-setup/deploy.sh
 ```
 
-This will provision all resources **and** automatically write your `.env` file to the repository root as `.env`.
+This will provision all resources **and** automatically write your `.env` file to the repository root as `.env`. The deployment will take a couple of minutes to complete.
 
 ## Verify the creation of your resources
 
 Go to the [Azure Portal](https://portal.azure.com/) and find your resource group, which should now contain resources like this:
 
 ![Azure Portal Resources](./images/azure-portal-resources.png)
 
-Go to the [Microsoft Foundry Portal](https://ai.azure.com) and verify that you can access the Foundry project.
+> [!NOTE]
+> The resource name prefixes vary by scenario and the suffixes are unique for each deployment
+
+Go to the [Microsoft Foundry Portal](https://ai.azure.com/nextgen) and verify that you can access the Foundry project.
 
 ![Foundry Project](./images/foundry-project.png)
 
 Select **Build** in the top navigation, then **Models**, and verify that the **gpt-5.4** model is deployed.
 
+>[!NOTE]
+> In some versions of the Foundry Portal the **Models** tab is rebranded to **Deployments** but they serve the same purpose.
+
 ![Foundry Model](./images/foundry-model.png)
 
 Select **gpt-5.4**, enter a test message in the model playground, and verify that you get a response.