What the best way to develop an AI Agent specific for Mental Health treatment ? Advise me on how to do it right !

[Priolli]

Hello everyone !

Its has been a while i have this call in my mind, i was hoping i could get some advisement from you all who are specialist, or maybe some help..

I want to develop an AI agent specific for Mental Health for people with problems on behavioral addictive, gambling, sports betting.
Of course the Agent must be able to give support on other demands the user may have about his personal mental state. But the main focus here is the gambling which has become a health epidemic around the world. Specially on countries which the education and opportunities are restrict.

For now, i have prepared one "super prompt" for my model, and upload some material like manuals for treatment on this subject for RAG on my model. But i dont want to stop there....

I was actually developing on the end of 2024, this same project using a different approach, using supabase as my database to create the log for the user context, to make it available all the time for my Agent, so the conversation can go on smoothly. Also, the data collect and stored on the supabase, help me create one Dashboard for the user to follow his advancements and steps back. The data i had, also was used to create one radar of critical state of the user, helping the platform to to ask for external help if the user has reached a step in which he has high possibility of something really serious for his life to happen. The plataform also collected financial information and served the user back if financial consulting to help him out put his bills on the place. Well, until there, i was doing most of the project by hand, and most of the stuff was not working to its full potential. The Pro-active engine i try to put on the platform for my Agent, proactive reach my user, utilizing the context data, asking how he is, or how is today goal doing, never worked. At this step, my project code got to confuse, my subscription to supabase to expensive, and i myself really frustrated, because i could only work with GPT 3.5 at that time for my personal financial reasons.

Well, we are on 2025, New technologies are released, Agentic AI has advanced on an amazing rhythm. I now i have time again to work on a personal project in which i could impact positively the society ! Those things said, i am still very noob coding, and honestly, i have been studying a lot in the past 2 weeks to put my knowledge uptodate! but i really want to ask you guys, what is the best way to develop this project ? i really want to make it right and help the people who need to be helped! who would you develop this project ? Which technologies would you use? Which tools to use as well.. This is really important!

Anyway, thanks in advance for those you have the patience to read the big text explaining my project situation, and special thanks for those who could share some of their knowledge with me !

Regards.

[leestott]

Hi @Priolli

Core Architecture with Azure AI Agents

1. Agent Definition & Capabilities
   Use Azure AI Agents to define a primary mental health agent with specialized skills:

• Core Skill: Behavioral addiction support (gambling, betting)
• Secondary Skills: General mental health check-ins, financial coaching, crisis escalation
• Tools: Connect to external APIs (e.g. financial dashboards, crisis helplines), RAG over treatment manuals, journaling prompts

You can modularize these using agent orchestration, allowing the main agent to delegate to sub-agents (e.g. “Finance Coach,” “Crisis Monitor,” “Daily Check-in”).

2. Contextual Memory & User Logs
   Instead of Supabase, use Azure Cosmos DB or Azure Table Storage to store:

• Session logs
• User goals and progress
• Financial data (with encryption)
• Risk scores and escalation flags

Pair this with Azure AI Agents’ memory feature to maintain persistent context across sessions. You can define memory scopes (short-term vs long-term) and use custom memory schemas to track behavioral patterns.

3. Proactive Agent Behavior
   Azure AI Agents now supports event-driven invocation:

• Use Azure Event Grid or Azure Logic Apps to trigger agent outreach based on:
  • Missed check-ins
  • Financial anomalies
  • Risk score thresholds
• Example: “Hi Lee, I noticed you haven’t logged a journal entry in 3 days. Want to talk about how you’re feeling today?”

You can also schedule proactive nudges using Azure Durable Functions for timed workflows.

4. RAG & Treatment Material Integration
   Use Azure AI Search with vector indexing to power retrieval-augmented generation:

• Upload manuals, CBT guides, and addiction recovery protocols
• Index with semantic search and chunking
• Connect via the Azure AI Agents RAG tool to enable contextual responses

You can even tag documents with metadata (e.g. severity level, topic) to fine-tune retrieval relevance.

5. Dashboard & Analytics
   For user-facing insights:

• Use Power BI Embedded or Azure Static Web Apps to build dashboards
• Pull data from Cosmos DB or Azure SQL
• Visualize:
  • Progress over time
  • Financial health
  • Risk radar
  • Goal tracking

For internal monitoring, set up Azure Monitor and Application Insights to track agent performance and user engagement.

6. Security & Compliance
   Mental health data is sensitive. Use:

• Azure Key Vault for secrets
• Managed Identity for secure service-to-service calls
• Role-Based Access Control (RBAC) for data segregation
• Microsoft Purview for data governance and compliance tagging

You can also integrate Azure Confidential Ledger for tamper-proof audit trails if needed.

Suggested Tech Stack

Agent Framework | Azure AI Agents | Define, orchestrate, and deploy agents
Memory & Logs | Azure Cosmos DB / Table Storage | Store user context and behavioral data
RAG | Azure AI Search + Blob Storage | Retrieve treatment materials and guides
Proactive Engine | Event Grid + Durable Functions | Trigger outreach based on user signals
Dashboard | Power BI Embedded / Static Web | Visualize progress and risk
Security | Key Vault + RBAC + Purview | Secure and govern sensitive data

Next Steps

1. Start with a single agent focused on gambling addiction support
2. Define a memory schema for user context (goals, risk score, financial status)
3. Upload treatment materials and configure Azure AI Search
4. Build a simple dashboard using Power BI or Static Web Apps
5. Add event-driven nudges using Logic Apps or Durable Functions

Resources

Azure AI Agents core

• Overview: https://learn.microsoft.com/azure/ai-services/agents/
• Quickstart (create and run an agent): https://learn.microsoft.com/azure/ai-services/agents/get-started-quickstart
• Concepts (tools, orchestration, memory, grounding): https://learn.microsoft.com/azure/ai-services/agents/concepts
• Use tools and function calling: https://learn.microsoft.com/azure/ai-services/agents/how-to/tools
• Manage agents in Azure AI Foundry (projects, deployments): https://learn.microsoft.com/azure/ai-studio/what-is-ai-studio
• Evaluate agents (quality, safety, groundedness): https://learn.microsoft.com/azure/ai-studio/concepts/evaluation

Memory, data storage, and contextual logs

• Azure Cosmos DB (general): https://learn.microsoft.com/azure/cosmos-db/
• Cosmos DB client-side encryption (protect sensitive fields): https://learn.microsoft.com/azure/cosmos-db/how-to-client-side-encryption
• Cosmos DB change feed (react to user events): https://learn.microsoft.com/azure/cosmos-db/change-feed
• Azure Table Storage overview: https://learn.microsoft.com/azure/storage/tables/table-storage-overview
• Design for sensitive data (Azure security baseline): https://learn.microsoft.com/security/benchmark/azure/baselines/cognitive-services-security-baseline

Proactive workflows and event-driven nudges

• Azure Event Grid (events backbone): https://learn.microsoft.com/azure/event-grid/overview
• Azure Logic Apps (no-code orchestration): https://learn.microsoft.com/azure/logic-apps/logic-apps-overview
• Azure Functions timer trigger (scheduled check-ins): https://learn.microsoft.com/azure/azure-functions/functions-bindings-timer
• Durable Functions (stateful orchestrations): https://learn.microsoft.com/azure/azure-functions/durable/durable-functions-overview
• Cosmos DB trigger for Functions (ingest signals, recompute risk): https://learn.microsoft.com/azure/azure-functions/functions-bindings-cosmosdb-v2

RAG over treatment and recovery materials

• Azure AI Search overview: https://learn.microsoft.com/azure/search/
• Vector search (Embeddings + hybrid retrieval): https://learn.microsoft.com/azure/search/vector-search-overview
• Semantic search (relevance improvement): https://learn.microsoft.com/azure/search/semantic-search-overview
• Indexers and data sources (Blob, metadata): https://learn.microsoft.com/azure/search/search-indexer-overview
• “Use your data” RAG pattern (Azure OpenAI/Azure AI Foundry): https://learn.microsoft.com/azure/ai-services/openai/how-to/use-your-data
• Popular end-to-end RAG sample (Search + OpenAI): https://github.com/Azure-Samples/azure-search-openai-demo

Observability, analytics, and user dashboards

• Azure Monitor (end-to-end telemetry): https://learn.microsoft.com/azure/azure-monitor/overview
• Application Insights (traces, metrics, usage): https://learn.microsoft.com/azure/azure-monitor/app/app-insights-overview
• OpenTelemetry with Azure Monitor (vendor-neutral): https://learn.microsoft.com/azure/azure-monitor/opentelemetry/overview
• Power BI Embedded (secure analytics in app): https://learn.microsoft.com/power-bi/developer/embedded/
• Azure Static Web Apps (lightweight dashboards): https://learn.microsoft.com/azure/static-web-apps/overview

Security, governance, and clinical safety guardrails

• Azure Key Vault (secrets, keys): https://learn.microsoft.com/azure/key-vault/general/overview
• Managed identity (secure service-to-service): https://learn.microsoft.com/entra/identity/managed-identities-azure-resources/overview
• Azure RBAC (least-privilege access): https://learn.microsoft.com/azure/role-based-access-control/overview
• Microsoft Purview (data governance, lineage): https://learn.microsoft.com/purview/overview
• Azure Confidential Ledger (tamper-evident audit): https://learn.microsoft.com/azure/confidential-ledger/overview
• Azure API Management (front external APIs safely): https://learn.microsoft.com/azure/api-management/api-management-key-concepts
• Azure AI Content Safety (harmful content, crisis detection): https://learn.microsoft.com/azure/ai-services/content-safety/overview
• Safety and evaluations in Azure AI Foundry: https://learn.microsoft.com/azure/ai-studio/how-to/evaluate-safety
• Responsible AI resources (patterns, assessments): https://learn.microsoft.com/azure/responsible-ai/
• Compliance offerings (HIPAA, ISO, NHS-aligned regs): https://learn.microsoft.com/compliance/regulatory/offering-home

Bonus: deployment and automation (for traceability)

• Bicep (IaC for reproducible environments): https://learn.microsoft.com/azure/azure-resource-manager/bicep/overview
• Azure DevOps Pipelines (CI/CD): https://learn.microsoft.com/azure/devops/pipelines/get-started/what-is-azure-pipelines
• GitHub Actions for Azure (CI/CD templates): https://learn.microsoft.com/azure/developer/github/github-actions
• Cost management and budgeting: https://learn.microsoft.com/azure/cost-management-billing/cost-management-billing-overview