Copilot Learning Hub: Your Gateway to Mastering Microsoft Copilot
Have you ever wanted a single place to go to learn all about Microsoft Copilot? The Copilot Learning Hub is designed to be your go-to source for everything related to Microsoft Copilot, with articles, videos, and hands-on labs for all tech areas.
Introducing Vector Search Similarity Capabilities in Azure Cache for Redis Enterprise
The latest wave of generative AI, like large language models, has paved the way for significant advancements in the utilization of vector embeddings and vector similarity search. Large language models, such as OpenAI's GPT, are capable of learning complex patterns and representations from vast amounts of text, enabling them to generate rich semantic embeddings for words, sentences, and documents. By leveraging these learned embeddings, developers can now harness the power of vector similarity search, revolutionizing the way information is organized, retrieved, and analyzed in various domains, including fraud detection, recommendation systems, and information retrieval.
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 StandupHack Together: RAG Hack - Building RAG Applications with LangChain.js
In the rapidly evolving landscape of Artificial Intelligence and Natural Language Processing, the use of Retrieval Augmented Generation (RAG) has emerged as a powerful solution to enhance the accuracy and relevance of responses generated by language models. In this article, we will explore the talk given during the Hack Together: RAG Hack event, where Glaucia Lemos, a Cloud Advocate at Microsoft, and Yohan Lasorsa, a Senior Cloud Advocate at Microsoft, demonstrated how LangChain.js is revolutionizing the development of RAG applications, making it easier to create intelligent applications that combine large language models (LLMs) with your own data sources.
