“2025 is the year of the AI agents.” — Satya Nadella 

AI agents are no longer just chatbots—they are intelligent collaborators that can reason, use tools, and work together to solve real problems. Microsoft offers a full stack of frameworks to help you build these agents, from low-code copilots in Teams to enterprise-grade orchestration with Azure AI Foundry, Semantic Kernel, and AutoGen. 

The Big Picture 

What’s in the stack? 

• Azure AI Foundry Agent Service – Managed, enterprise‑grade runtime for hosting agents, tools, threads, observability. 

• Semantic Kernel – provides a unified Agent API plus orchestration patterns (sequential, hand‑off, group chat) with plugins/tools in C#, Python, and Java. 

• AutoGen – Open-source research‑friendly framework for conversable multi‑agent teams with an event‑driven core and a no‑code AutoGen Studio UI. 

• Copilot Studio Agents – Bring low‑code makers, connectors, agent flows, and Microsoft 365 extensibility into the same ecosystem. 

Azure AI Foundry Agent Service 

 

 

 

What it is: A managed service that hosts your agents, organizes conversations, and gives them built‑in tools like file search, code execution, and web grounding. It also supports connected agents, so multiple agents can work together. 

Why use it: You do not have to worry about infrastructure, security, or scaling. Everything runs in Azure with enterprise‑grade controls. 

How it works: Each agent has three core components: 

• Model (LLM): Powers reasoning and language understanding. 

• Instructions: Define the agent’s goals, behavior, and constraints. 

• Tools: Let the agent retrieve knowledge or take some action. 

AI Foundry Agent Service Components: 

Practical Example scenarios:  

• Ops Analyst Agent: Drop a CSV; the agent uses Code Interpreter to cluster incidents, charts spikes, and proposes playbook steps.  

• Policy Concierge: Upload policy PDFs to File Search; the agent answers with citations and links, grounded by Bing where allowed. 

• Connected Agents (Multi‑Agent): Primary agent delegates to a specialist agent for pricing, another for compliance; returns a consolidated answer. 

Semantic Kernel 

What it is: A programming library that gives developers a unified way to build agents, add tools, and control how multiple agents work together. Semantic Kernel is a lightweight, open-source development kit that lets you easily build AI agents and integrate the latest AI models into your C#, Python, or Java codebase. 

Why use it: You want a unified Agent API, plugin‑based tools, and first‑class orchestration patterns (like handoff and group chat) in code—and you want the option to run locally or publish to managed Agent Service later. 

Multi-Agent Orchestration Patterns:  

• Sequential pipeline: Researcher → Writer → Reviewer for compliant document creation. 

• Handoff: Generalist → Specialist when a rule or detection triggers escalation. 

• Group Chat: Coordinated brainstorming with a manager agent.\ All patterns are provided in the SDK (experimental label on some features—watch the docs. 

 

 

 

Semantic Kernel Components:  

Practical Example scenarios:  

• RAG + Policy Checker: Retrieval agent produces evidence; checker agent enforces policy; publisher agent formats the eventual answer. 

• ETL Code Helper: Coding agent writes data transforms; execution plugin runs tests; reviewer agent ensures style and governance. 

• Ticket Summarizer → Fix Planner: One agent summarizes context; another proposes a remediation plan and rollback steps. 

AutoGen 

What it is: AutoGen is an open-source framework designed by Microsoft Research’s AI Frontiers Lab to build AI agent systems. It simplifies the creation and orchestration of event-driven, distributed agentic applications, enabling multiple LLMs and SLMs, tools, and advanced multi-agent design patterns. AutoGen supports scenarios where multiple agents interact with each other to complete complex tasks autonomously or with human oversight. The event-driven and distributed architecture makes it suitable for workflows that require long-running autonomous agents that collaborate across information boundaries with variable degrees of human involvement. AutoGen currently supports C# and Python. It even has a UI called ‘AutoGen Studio’ for no‑code experiments. 

Why use it? 
Perfect for experimenting with multi-agent strategies before production. 

 

 AutoGen Components:  

Example scenarios:  

• Research and experimentation on multi-agent collaboration patterns. 

NOTE: AutoGen is an open-source project maintained by Microsoft Research’s AI Frontiers Lab with the help of the community. When this article is published, AutoGen is currently in its 0.7.4 version. It is designed for you to experiment with various multi agent systems and patterns before deploying similar systems in production. Support is currently provided through GitHub Discussions and Discord server. If you are using AutoGen’s multi-agent runtime, you can later transition to Semantic Kernel for an enterprise-ready environment without the overhead of building production infrastructure yourself. Alternatively, if you prefer to use AutoGen for enterprise use within your solution, you can continue leveraging it with community support. This path is often chosen when there is a need for advanced agentic patterns that are not yet available in Semantic Kernel or other products. 

Copilot Studio Agents 

 

 

 

What it is: Copilot Studio is Microsoft’s low‑code environment for designing, testing, governing, and publishing AI agents (copilots). It lets you build agents that can reason over enterprise knowledge, call tools, and automate workflows—then publish them to Microsoft 365 surfaces (e.g., Teams) and beyond with tenant‑level controls. 

Why use it? 

• Safe and managed: Built-in security, governance, and cost controls. Aligned to Microsoft 365 compliance and management mode.  

• Blend low‑code & pro‑code: Start from natural‑language authoring and add agent flows, connectors, and pro‑code actions or custom APIs as your use case grows in complexity. 

• Integrate where users work: Publish to Teams and integrate with Microsoft 365 data; you can also extend Microsoft 365 Copilot with your own agents. 

• Modern tooling options: Bring in advanced tools such as Model Context Protocol (MCP) for external tools and Computer Use for UI automation—all under admin oversight. 

 

Practical examples 

• IT Helpdesk Front Door: Collects context, searches KB, files a ticket, posts updates to Teams. 

• Order Cancellation Flow: Calls ERP via connector, confirms policy, captures reason codes—end to end in an agent flow. 

• HR FAQ + Case Create: Grounds on HR docs, hands off to a flow when PII is detected for secure handling. 

End-to-End Example 

Contoso Manufacturing (“Agents Everywhere”) 

Context: Contoso builds smart HVAC systems for commercial buildings. They need agents for (1) field support in Teams, (2) ops triage on files/telemetry, (3) engineering RAG with policy checks, and (4) rapid multi‑agent experiments to discover new patterns. 

How we build each agent: 

A) Teams Support Copilot (Copilot Studio)

• Write clear instructions for the agent and connect it to company knowledge (e.g., SharePoint). 

• Add actions and connectors so it can create tickets or check parts. 

• Use Agent Flows for step-by-step tasks and publish the agent in Teams with security and cost controls. 

B) Ops Triage Agent (Azure AI Foundry Agent Service)

• Create an agent that can search files and run code for analysis. 

• Use Connected Agents to delegate tasks to specialists. 

• Track every step using built-in logs for auditing. 

C) Engineering RAG + Policy Checker (Semantic Kernel)

• Connect to AI services and vector databases for retrieval. 

• Use orchestration patterns like Sequential or Handoff to manage workflows. 

• Add filters for safety checks and enable observability for monitoring. 

D) Multi-agent Experiments (AutoGen)

• Build teams of agents using AgentChat and add tools via extensions. 

• Test collaboration styles with AutoGen Bench to find the best approach. 

• Move successful designs into Semantic Kernel or Agent Service for production. 

Agent Lifecycle with Microsoft AI Frameworks 

An agent lifecycle is the step-by-step process an AI agent goes through from the moment you plan it until it is running and improving in production. Here is a simple breakdown: 

1. Discover & Design – Define goals, risks, and tools; pick the right platform (Copilot Studio, SK, Agent Service, AutoGen). 

     2. Build –  

• • Copilot Studio: Create topics, flows, and connectors. 

• • SK: Add connectors, plugins, and filters. 

• • Agent Service: Configure tools and connected agents. 

• • AutoGen: Use AgentChat and Studio for quick tests. 

    3. Evaluate & Harden – Test functionality, enforce safety filters, and validate tool permissions. 

   4. Deploy – Publish to Teams or run in managed environments. 

   5. Operate & Observe – Monitor performance, costs, and logs. 

   6. Evolve – Promote proven AutoGen patterns to SK, refresh vector stores/knowledge, add new connected agents as needs grow. 

Operationalize Agents in Your Organization 

Operationalizing agents in an organization means moving from experiments to production-ready, governed solutions that deliver measurable value. Here is a clear, practical approach: 

• Fit the tool to the job: Copilot Studio (frontline, low‑code + governance), Agent Service (managed runtime, threads → runs → steps; connected agents), Semantic Kernel (pro‑code orchestration: connectors, plugins, filters, observability), AutoGen (prototype multi‑agent patterns). 

• Lifecycle you can reuse: Discover & design → Build → Evaluate & harden → Deploy → Operate & observe → Evolve. 

• Governance & auditability built‑in: Copilot Studio environments/DLP/cost controls; Agent Service run/step traces; SK filters + OpenTelemetry; scoped tools & data. 

• Grounding & actions: Ground with SharePoint/Dataverse/Azure AI Search; act via Actions & Connectors (Copilot Studio) or Plugins/Functions (SK); add MCP tools when needed. 

• Path to production: Prove collaboration in AutoGen → standardize orchestration in SK → run at scale on Agent Service → publish user experiences via Copilot Studio. 

• Outcomes to track: Ticket deflection, time‑to‑resolution, content quality & compliance rate, user adoption, cost per task. 

Agents Are Here to Stay 

From low-code copilots to enterprise-grade orchestration, Microsoft’s agent frameworks give you everything you need to build smart, scalable, and secure AI agents. Whether you are experimenting with AutoGen, orchestrating with Semantic Kernel, deploying with Azure AI Foundry, or publishing in Copilot Studio—2025 is the year to operationalize agents and turn ideas into impact. 

References 

👉 Which framework will you start with? Share your thoughts in the comments! 
📌 Explore the docs: 

• Azure AI Foundry , 
• Semantic Kernel  
• AutoGen 
• CoPilot 

Tags: 

#AzureAI #AI #Agents #Copilot #SemanticKernel #AutoGen #AzureOpenAI #TechCommunity #MicrosoftLearn #SkilledByMTT