DEV Community: Deep Sharma

Building a Live Transcript

Deep Sharma — Fri, 08 Aug 2025 17:04:33 +0000

I have hooked myself to build a solution for this ..

High Level AI agent framework which will fit into most modern and headless model to adopt .

The problem is pool of profiles and identifying the speaker and laebling , next is if there are two speakers how to differentiate if they speak randomly.

Bridging AI and Dynamics: A Scalable Architecture for Intent-Driven Applications

Deep Sharma — Mon, 14 Jul 2025 12:31:15 +0000

Introduction

Modern digital ecosystems demand more than just responsive design - they call for intelligent, secure, and integrated workflows. The architecture outlined here showcases a streamlined, scalable approach to building AI-enhanced web applications that interact seamlessly with Microsoft's Dynamics 365 CRM. From natural language processing to secure authentication and modular services, this setup demonstrates how strategic design choices can unlock operational efficiency across platforms.

Architecture Breakdown
Client Application Layer
Tools like Power Apps, Postman, mobile apps, and custom-built interfaces interact with the server via HTTP requests.
These clients serve varied use cases - from manual agent querying to CRM record automation.

Express.js API Server
Acts as the central processing hub using middleware such as Helmet, Body Parser, and CORS.
Incorporates robust routing and global error handling with health check endpoints to ensure uptime.

Controller Layer
Manages core CRUD logic (GET, CREATE, UPDATE, DELETE) plus an ANALYZE function for AI-driven tasks.
Performs validation, routing, and intent analysis based on request type and input parameters.

AI Service
Built on OpenAI GPT-4, this layer identifies user intent and extracts relevant entities using NLP.
Confidence levels and actions are returned, streamlining downstream processes.

Dynamics Service
Operates via Axios, enabling efficient OData v4 queries (select, filter, order).
CRUD operations are directly mapped to Dynamics 365 entities like contacts, accounts, and opportunities.

Auth Service & Azure AD
OAuth 2.0 flow is managed with JWT token caching and auto-refresh features.
Ensures secure multi-tenant access using Azure Active Directory, validating client credentials.

Demo Service
Supplies mock data for non-production testing scenarios, allowing agile front-end development.

Configuration Layer
Handles environment-specific values with validation rules and default settings, ensuring portability and consistency.

Conclusion
This modular architecture provides a robust foundation for intelligent applications that blend AI-driven decision-making with CRM data orchestration. With a clean separation of concerns, secure authentication, and dynamic data flow, the system supports both scale and adaptability - making it a compelling blueprint for enterprise-ready innovation.

Github --https://github.com/anshdeepsharma/multiagentsd365

Build an AI-Powered Agent for Dynamics 365 using Node.js and OpenAI

Deep Sharma — Fri, 11 Jul 2025 20:24:33 +0000

Hey devs! Want to build an AI agent that talks to your Microsoft Dynamics 365 instance in plain English?

In this post, I’ll show you how I built a full-stack backend agent that interprets natural language queries, securely connects to Dynamics 365, and performs real-time CRM operations.

Features
Natural language → Structured CRM actions
OpenAI-powered intent parsing
Dynamics 365 Web API integration

Full CRUD support
Azure AD OAuth 2.0 authentication

Tech Stack
Node.js 18+
TypeScript
Express
Axios
OpenAI API
Azure AD + MSAL

Azure Setup
Register an app in Azure AD
Add user_impersonation for Dynamics + User.Read for Graph
Save your client_id, tenant_id, and client_secret
Environment Variables (.env)

envCopyEditAZURE_CLIENT_ID=...
AZURE_CLIENT_SECRET=...
AZURE_TENANT_ID=...
DYNAMICS_URL=https://yourorg.crm.dynamics.com
OPENAI_API_KEY=...

Project Structure
arduinoCopyEditsrc/
├── services/
│ ├── authService.ts
│ ├── dynamicsService.ts
│ └── aiService.ts
├── controllers/
│ └── agentController.ts
├── config.ts
└── app.ts

Example Query

bashCopyEditcurl -X POST http://localhost:3000/api/agent/query \
-H "Content-Type: application/json" \
-d '{"query": "Show me all contacts from Microsoft"}'

Response:

jsonCopyEdit{
"success": true,
"intent": {
"action": "get",
"entity": "contacts",
...
},
"result": {
"count": 3,
"data": [...]
}
}

Extend It
Add support for leads, cases, custom entities
Redis caching layer
Frontend chatbot
Webhook handler for Dynamics events

Deploy It

You can deploy to:
Azure App Service
Docker
Render / Railway / Fly.io
Add observability with Azure Application Insights or LogRocket.

Resources
Microsoft Graph + CRM API Docs
OpenAI API
OAuth 2.0 for Dynamics

linkedin--demo here https://github.com/anshdeepsharma/AIonD365/compare/master...deep-prac

Final Thoughts

This is a solid base for building real AI agents that work with enterprise systems. Whether you're automating support, sales ops, or reporting — AI + CRM is fire.