MentorAI is your AI peer—designed to guide, support, and empower you throughout your journey in the team. Whether you're onboarding, skilling up, navigating internal processes, or seeking help with team-specific tasks, MentorAI is always there to assist.
Built for remote and hybrid work environments, MentorAI delivers personalized, role-based learning journeys and real-time guidance. It integrates seamlessly with internal resources and uses Azure AI technologies to provide contextual support tailored to your role, team, and goals.
Think of it as a mentor you can reach out to anytime—whether you're part of a mission-critical team or working in SFMC. It won’t overwhelm you with tons of information. Instead, it offers just what you need, when you need it, helping you stay engaged, confident, and successful.
This solution deploys a web-based chat application with an AI agent running in Azure Container App.
The agent leverages the Azure AI Agent service and utilizes Azure AI Search for knowledge retrieval from uploaded files, enabling it to generate responses with citations. The solution also includes built-in monitoring capabilities with tracing to ensure easier troubleshooting and optimized performance.
This solution creates an Azure AI Foundry project and Azure AI services. More details about the resources can be found in the resources documentation. There are options to enable logging, tracing, and monitoring.
Instructions are provided for deployment through GitHub Codespaces, VS Code Dev Containers, and your local development environment.
The app code runs in Azure Container App to process the user input and generate a response to the user. It leverages Azure AI projects and Azure AI services, including the model and agent.
MentorAI offers a rich set of features designed to make learning and onboarding seamless, personalized, and effective:
- Personalization: MentorAI adapts its responses and learning paths based on the user's role, team, and goals. Whether you're in a mission-critical team or SFMC, the agent delivers relevant, focused support without overwhelming you.
- Real-Time Guidance: Provides contextual help for navigating internal tools, understanding processes, and progressing through certification pathways. It acts like a knowledgeable peer who’s always available.
- Integration with Internal Resources: Seamlessly connects to internal systems like SharePoint, Teams, and organizational APIs to surface the most relevant content and automate answers.
- Performance Nudges: Offers proactive suggestions and reminders to help users stay on track with learning goals, certifications, and team expectations.
- Scalable Architecture: Designed to be modular and extensible, MentorAI can easily be adapted to support new roles, departments, and organizational needs.
- Knowledge Democratization: Makes expert insights accessible to everyone, reducing dependency on tribal knowledge and enabling faster ramp-up times for new employees.
- Conversational Interface: Intuitive chat experience for seamless interaction
Here is a screenshot showing the chatting web application with requests and responses between the system and the user:
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- Click
Open in GitHub CodespacesorDev Containersbutton above - Wait for the environment to load
- Run the following commands in the terminal:
azd up
- Follow the prompts to select your Azure subscription and region
- Wait for deployment to complete (5-20 minutes) - you'll get a web app URL when finished
For detailed deployment options and troubleshooting, see the full deployment guide. After deployment, try these sample questions to test your agent.
For developers who want to run the application locally or customize the agent:
- Local Development Guide - Set up a local development environment, customize the frontend (starting with AgentPreview.tsx), modify agent instructions and tools, and use evaluation to improve your code.
This guide covers:
- Environment setup and prerequisites
- Running the development server locally
- Frontend customization and backend communication
- Agent instructions and tools modification
- File management and agent recreation
- Using agent evaluation for code improvement
To prevent incurring unnecessary charges, it's important to clean up your Azure resources after completing your work with the application.
-
When to Clean Up:
- After you have finished testing or demonstrating the application.
- If the application is no longer needed or you have transitioned to a different project or environment.
- When you have completed development and are ready to decommission the application.
-
Deleting Resources: To delete all associated resources and shut down the application, execute the following command:
azd down
Please note that this process may take up to 20 minutes to complete.
Pricing varies per region and usage, so it isn't possible to predict exact costs for your usage. The majority of the Azure resources used in this infrastructure are on usage-based pricing tiers.
You can try the Azure pricing calculator for the resources:
- Azure AI Foundry: Free tier. Pricing
- Azure Storage Account: Standard tier, LRS. Pricing is based on storage and operations. Pricing
- Azure AI Services: S0 tier, defaults to gpt-4o-mini. Pricing is based on token count. Pricing
- Azure Container App: Consumption tier with 0.5 CPU, 1GiB memory/storage. Pricing is based on resource allocation, and each month allows for a certain amount of free usage. Pricing
- Log analytics: Pay-as-you-go tier. Costs based on data ingested. Pricing
- Agent Evaluations: Incurs the cost of your provided model deployment used for local evaluations.
- AI Red Teaming Agent: Leverages Azure AI Risk and Safety Evaluations to assess attack success from the automated AI red teaming scan. Users are billed based on the consumption of Risk and Safety Evaluations as listed in our Azure pricing page. Click on the tab labeled “Complete AI Toolchain” to view the pricing details.
azd down.
This template also uses Managed Identity for local development and deployment.
To ensure continued best practices in your own repository, we recommend that anyone creating solutions based on our templates ensure that the Github secret scanning setting is enabled.
You may want to consider additional security measures, such as:
- Enabling Microsoft Defender for Cloud to secure your Azure resources.
- Protecting the Azure Container Apps instance with a firewall and/or Virtual Network.
Important Security Notice
This template, the application code and configuration it contains, has been built to showcase Microsoft Azure specific services and tools. We strongly advise our customers not to make this code part of their production environments without implementing or enabling additional security features.
For a more comprehensive list of best practices and security recommendations for Intelligent Applications, visit our official documentation.
This template creates everything you need to get started with Azure AI Foundry:
| Resource | Description |
|---|---|
| Azure AI Project | Provides a collaborative workspace for AI development with access to models, data, and compute resources |
| Azure OpenAI Service | Powers the AI agents for conversational AI and intelligent search capabilities. Default models deployed are gpt-4o-mini, but any Azure AI models can be specified per the documentation |
| Azure Container Apps | Hosts and scales the web application with serverless containers |
| Azure Container Registry | Stores and manages container images for secure deployment |
| Storage Account | Provides blob storage for application data and file uploads |
| AI Search Service | Optional - Enables hybrid search capabilities combining semantic and vector search |
| Application Insights | Optional - Provides application performance monitoring, logging, and telemetry for debugging and optimization |
| Log Analytics Workspace | Optional - Collects and analyzes telemetry data for monitoring and troubleshooting |
For solutions to common deployment, container app, and agent issues, see the Troubleshooting Guide.
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