Building your own copilot – yes, but how? (Part 1 of 2)
Are you interested in building your own AI co-pilot? Check out the first of a two-part blog post from Carlotta Castelluccio that covers the basics of creating a virtual assistant that can help you with tasks like scheduling, email management, and more. Learn about the tools and technologies involved, including Microsoft's Bot Framework and Language Understanding Intelligent Service (LUIS). Whether you're a software developer or just curious about the possibilities of AI, this post is a great introduction to building your own co-pilot.Microsoft Build 2024: Essential Guide for AI Developers at Startups and Cloud-First Companies
Generative AI is advancing fast, with OpenAI’s GPT-4o leading the way. GPT-4o boasts improved multilingual understanding, faster responses, lower costs, and real-time processing of text, audio, and images. This boosts new Generative AI (GenAI) use cases. Explore cutting-edge solutions like models, frameworks, vector databases, and LLM observability platforms. Born-in-the-cloud companies are at the forefront of this AI revolution. Be part of the future at Microsoft Build 2024!Building Retrieval Augmented Generation on VSCode & AI Toolkit
LLMs usually have limited knowledge about specific domains. Retrieval Augmented Generation (RAG) helps LLMs be more accurate and give relevant output to specific domains and datasets. We will see how we can do this for local models using AI Toolkit,Building Intelligent Applications with Local RAG in .NET and Phi-3: A Hands-On Guide
Let's learn how to do Retrieval Augmented Generation (RAG) using local resources in .NET! In this post, we’ll show you how to combine the Phi-3 language model, Local Embeddings, and Semantic Kernel to create a RAG scenario.Make your own private ChatGPT
Introduction Creating your own private ChatGPT allows you to leverage AI capabilities while ensuring data privacy and security. This guide walks you through building a secure, customized chatbot using tools like Azure OpenAI, Cosmos DB and Azure App service. Why Build a Private ChatGPT? With the rise of AI-driven applications, organizations, people often face challenges related to data privacy, customization, and integration. Building a private ChatGPT addresses these concerns by: Maintaining Data Privacy: Keep sensitive information within your infrastructure. Customizing Responses: Tailor the chatbot’s behavior and language to suit your requirements. Ensuring Security: Leverage enterprise-grade security protocols. Avoiding Data Sharing: Prevent your data from being used to train external models. If organizations do not take these measures their data may go into future model training and can leak your sensitive data to public. Eg: Chatgpt collects personal data mentioned in their privacy policy Prerequisites Before you begin, ensure you have: Access to Azure OpenAI Service. A development environment set up with Python. Basic knowledge of FastAPI and MongoDB. An Azure account with necessary permissions. If you do not have Azure subscription, try Azure for students for FREE. Step 1: Set Up Azure OpenAI Log in to the Azure Portal and create an Azure OpenAI resource. Deploy a model, such as GPT-4o (multimodal), and note down the endpoint and API key. Note there is also an option of keyless authentication. Configure permissions to control access. Step 2: Use Chatgpt like app sample You can select any repository to be as base template for your app, in this I will be using the third option AOAIchat. It is developed by me. GitHub - mckaywrigley/chatbot-ui: AI chat for any model. Azure-Samples/azure-search-openai-demo: A sample app for the Retrieval-Augmented Generation pattern running in Azure, using Azure AI Search for retrieval and Azure OpenAI large language models to power ChatGPT-style and Q&A experiences. sourabhkv/AOAIchat: Azure OpenAI chat This architecture diagram represents a typical flow for a private ChatGPT application with the following components: App UX (User Interface): This is the front-end application (mobile, web, or desktop) where users interact with the chatbot. It sends the user's input (prompt) and displays the AI's responses. App Service: Acts as the backend application, handling user requests and coordinating with other services. Functions: Receives user inputs and prepares them for processing by the Azure OpenAI service. Streams AI responses back to the App UX. Reads from and writes to Cosmos DB to manage chat history. Azure OpenAI Service: This is the core AI service, processing the user input and generating responses using models like GPT-4o. The App Service sends the user input (along with context) to this service and receives the AI-generated responses. Cosmos DB: A NoSQL database used to store and manage chat history. Operations: Writes user messages and AI-generated responses for future reference or analysis. Reads chat history to provide context for AI responses, enabling more intelligent and contextual conversations. Data Flow: User inputs are sent from the App UX to the App Service. The App Service forwards the input (with additional context, if needed) to Azure OpenAI. Azure OpenAI generates a response, which is streamed back to the App UX via the App Service. The App Service writes user inputs and AI responses to Cosmos DB for persistence. This architecture ensures scalability, secure data handling, and the ability to provide contextual responses by integrating database and AI services. What can you do with my template? AOAIchat supports personal, enterprise chat enabled by RAG People can enable RAG mode if they want to search within their database, else it behaves like normal ChatGPT. It supports multimodality, (supports image, text input) also depends on model deployed in Azure AI foundry. Step 3: Deploy to Azure Deploy a Cosmos DB account in nearest region Deploy Azure OpenAI model (gpt-4o, gpt-4o-mini recommended) Deploy Azure App service, try using container I would recommend B1plan to your nearest region, select docker registry sourabhkv/aoaichatdb:0.1 startup command uvicorn app:app --host 0.0.0.0 --port 80 After app service starts, put all environment variables The application requires the following environment variables to be set for proper configuration: Environment Variable Description AZURE_OPENAI_ENDPOINT The endpoint for Azure OpenAI API. AZURE_OPENAI_API_KEY API key for accessing Azure OpenAI. DEPLOYMENT_NAME Azure OpenAI deployment name. API_VERSION API version for Azure OpenAI. MAX_TOKENS Maximum tokens for API responses. MONGO_DETAILS MongoDB connection string. AZURE_OPENAI_ENDPOINT=<your_azure_openai_endpoint> AZURE_OPENAI_API_KEY=<your_azure_openai_api_key> DEPLOYMENT_NAME=<your_deployment_name> API_VERSION=<your_api_version> MAX_TOKENS=<max_tokens> MONGO_DETAILS=<your_mongo_connection_string> Optional feature: implement authentication to secure access. Within app service select Authentication and select service providers. I went with Entra based authentication with single tenant. There is option of multi-tenant, personal accounts as well. Restart App service and within 2 minutes your private ChatGPT is ready. Pricing Pricing may depend on the plan you have deployed resources and region. Check Azure calculator for price estimation. My estimate for pricing I deployed all my resources in Sweden central Cosmos DB config - Cosmos DB for MongoDB (RU) serverless config with single write master, 2 GB transactional storage, 2 backup plan (FREE) ~ 0.75$ Azure OpenAI service - plan S0, model gpt-4o-mini global deployment, Input 20000 tokens, Output 10000 tokens ~ 9.00$ App service plan - OS Linux, Tier B1, instance count 1 ~13.14$ Total monthly cost = 22.89$ This price may vary in future, in region I calculated my configuration in Azure calculator Governance Azure OpenAI provides content filters to block any kind of input that violates responsible AI practices. Categories include Hate and Fairness Sexual Violence Self-harm User Prompt Attacks (direct and indirect) The content filtering system detects and takes action on specific categories of potentially harmful content in both input prompts and output completions. Azure OpenAI Service includes default safety settings applied to all models set as medium. Content filters can be modified to different level depending on use case. It supports RAG, I have provided detailed solution for it in my GitHub. Practical implementation GE Aerospace, in partnership with Microsoft and Accenture, has launched a company-wide generative AI platform, leveraging Microsoft Azure and Azure OpenAI Service. This solution aims to transform asset tracking and compliance in aviation, enabling quick access to maintenance records and reducing manual processing time from days to minutes. It supports informed decision-making by providing insights into aircraft leasing, compliance gaps, and asset health. For enterprises implementing private ChatGPT solutions, this illustrates the potential of generative AI for streamlining document-intensive processes while ensuring data security and compliance through cloud-based infrastructure like Azure. GE Aerospace Launches Company-wide Generative AI Platform for Employees | GE Aerospace News Build your own private ChatGPT style app with enterprise-ready architecture - By Microsoft Mechanics How to make private ChatGPT for FREE? It can be FREE if all of the setup is running locally on your hardware. Cosmos DB <-> MongoDB. Azure OpenAI <-> Ollama / LM studio Refer this NOTE : I have used gpt-4o, gpt-4o-mini these values are hardcoded in webpage, if you are using other models, you might have to change them in index.html. App Service <-> Local machine Register for Github models to access API for FREE. Note: GitHub models have rate limit for different models. Useful links sourabhkv/AOAIchat: Azure OpenAI chat What is RAG? Get started with Azure OpenAI API Chat with Azure OpenAI models using your own data
The Future of Agentic AI: Inside Microsoft Agent Framework 1.0
Agentic AI is rapidly moving beyond demos and chatbots toward long‑running, autonomous systems that reason, call tools, collaborate with other agents, and operate reliably in production. On April 3, 2026, Microsoft marked a major milestone with the General Availability (GA) release of Microsoft Agent Framework 1.0, a production‑ready, open‑source framework for building agents and multi‑agent workflows in.NET and Python. [techcommun...rosoft.com] In this post, we’ll deep‑dive into: What Microsoft Agent Framework actually is Its core architecture and design principles What’s new in version 1.0 How it differs from other agent frameworks When and how to use it—with real code examples What Is Microsoft Agent Framework? According to the official announcement, Microsoft Agent Framework is an open‑source SDK and runtime for building AI agents and multi‑agent workflows with strong enterprise foundations. Agent Framework provides two primary capability categories: 1. Agents Agents are long‑lived runtime components that: Use LLMs to interpret inputs Call tools and MCP servers Maintain session state Generate responses They are not just prompt wrappers, but stateful execution units. 2. Workflows Workflows are graph‑based orchestration engines that: Connect agents and functions Enforce execution order Support checkpointing and human‑in‑the‑loop scenarios This leads to a clean separation of responsibilities: Concern Handled By Reasoning & interpretation Agent Execution policy & control flow Workflow This separation is a foundational design decision. High‑Level Architecture From the official overview, Agent Framework is composed of several core building blocks: Model clients (chat completions & responses) Agent sessions (state & conversation management) Context providers (memory and retrieval) Middleware pipeline (interception, filtering, telemetry) MCP clients (tool discovery and invocation) Workflow engine (graph‑based orchestration) Conceptual Flow 🌟 What’s New in Version 1.0 Version 1.0 marks the transition from "Release Candidate" to "General Availability" (GA). Production-Ready Stability: Unlike the earlier experimental packages, 1.0 offers stable APIs, versioned releases, and a commitment to long-term support (LTS). A2A Protocol (Agent-to-Agent): A new structured messaging protocol that allows agents to communicate across different runtimes. For example, an agent built in Python can seamlessly coordinate with an agent running in a .NET environment. MCP (Model Context Protocol) Support: Full integration with the Model Context Protocol, enabling agents to dynamically discover and invoke external tools and data sources without manual integration code. Multi-Agent Orchestration Patterns: Stable implementations of complex patterns, including: Sequential: Linear handoffs between specialized agents. Group Chat: Collaborative reasoning where agents discuss and solve problems. Magentic-One: A sophisticated pattern for task-oriented reasoning and planning. Middleware Pipeline: The new middleware architecture lets you inject logic into the agent's execution loop without modifying the core prompts. This is essential for Responsible AI (RAI), allowing you to add content safety filters, logging, and compliance checks globally. DevUI Debugger: A browser-based local debugger that provides a real-time visual representation of agent message flows, tool calls, and state changes. Code Examples Creating a Simple Agent (C#) From Microsoft Learn : using Azure.AI.Projects; using Azure.Identity; using Microsoft.Agents.AI; AIAgent agent = new AIProjectClient( new Uri("https://your-foundry-service.services.ai.azure.com/api/projects/your-project"), new AzureCliCredential()) .AsAIAgent( model: "gpt-5.4-mini", instructions: "You are a friendly assistant. Keep your answers brief."); Console.WriteLine(await agent.RunAsync("What is the largest city in France?")); This shows: Provider‑agnostic model access Session‑aware agent execution Minimal setup for production agents Creating a Simple Agent (Python) from agent_framework.foundry import FoundryChatClient from azure.identity import AzureCliCredential client = FoundryChatClient( project_endpoint="https://your-foundry-service.services.ai.azure.com/api/projects/your-project", model="gpt-5.4-mini", credential=AzureCliCredential(), ) agent = client.as_agent( name="HelloAgent", instructions="You are a friendly assistant. Keep your answers brief.", ) result = await agent.run("What is the largest city in France?") print(result) The same agent abstraction applies across languages. When to Use Agents vs Workflows Microsoft provides clear guidance: Use an Agent when… Use a Workflow when… Task is open‑ended Steps are well‑defined Autonomous tool use is needed Execution order matters Single decision point Multiple agents/functions collaborate Key principle: If you can solve the task with deterministic code, do that instead of using an AI agent. 🔄 How It Differs from Other Frameworks Microsoft Agent Framework 1.0 distinguishes itself by focusing on "Enterprise Readiness" and "Interoperability." Feature Microsoft Agent Framework 1.0 Semantic Kernel / AutoGen LangChain / CrewAI Philosophy Unified, production-ready SDK. Research-focused or tool-specific. High-level, developer-friendly abstractions. Integration Deeply integrated with Microsoft Foundry and Azure. Varied; often requires more glue code. Generally cloud-agnostic. Interoperability Native A2A and MCP for cross-framework tasks. Limited to internal ecosystem. Uses proprietary connectors. Runtime Identical API parity for .NET and Python. Primarily Python-first (SK has C#). Primarily Python. Control Graph-based deterministic workflows. More non-deterministic/experimental. Mixture of role-based and agentic. 🛠️ Key Technical Components Agent Harness: The execution layer that provides agents with controlled access to the shell, file system, and messaging loops. Agent Skills: A portable, file-based or code-defined format for packaging domain expertise. Implementation Tip: If you are coming from Semantic Kernel, Microsoft provides migration assistants that analyze your existing code and generate step-by-step plans to upgrade to the new Agent Framework 1.0 standards. Microsoft Agent Framework Version 1.0 | Microsoft Agent Framework Agent Framework documentation 🎯 Summary Microsoft Agent Framework 1.0 is the "grown-up" version of AI orchestration. By standardizing the way agents talk to each other (A2A), discover tools (MCP), and process information (Middleware), Microsoft has provided a clear path for taking AI experiments into production. For more detailed guides, check out the official Microsoft Agent Framework DocumentationMicrosoft Agent Framework - .NET AI Community Standup
