AI Agents: Exploring Agentic Frameworks - Part 2
Microsoft Tech Community | March 11, 2025 | AI Agents Series (Part 2 of 10)
Motive / Why I Wrote This
After introducing the foundational concepts of AI agents in the first part of this series, I recognized the need to provide developers with a concrete understanding of the frameworks they can use to build agentic systems. The landscape of agent development frameworks is evolving rapidly, with various options offering different approaches, capabilities, and tradeoffs.
I wrote this article to help developers navigate this complex landscape by providing a comprehensive comparison of leading agentic frameworks, with a focus on Microsoft's offerings: AutoGen, Semantic Kernel, and Azure AI Agent Service. Through my work with various organizations implementing agent solutions, I observed that choosing the right framework is often a critical but challenging decision that significantly impacts development efficiency, system capabilities, and long-term maintenance.
As the second installment in the AI Agents series, this article builds upon the conceptual foundation established in the first part to provide practical guidance on framework selection and implementation. By offering concrete code examples and architectural patterns across different frameworks, I aimed to equip developers with the knowledge needed to make informed decisions and get started with actual implementation.
Overview
The landscape of AI agent development frameworks is rapidly evolving, with various options offering different approaches to building intelligent, agentic systems. This article provides a comprehensive exploration of leading frameworks, with particular focus on Microsoft's offerings: AutoGen, Semantic Kernel, and Azure AI Agent Service. Through detailed comparisons, code examples, and architectural patterns, it equips developers with the knowledge needed to select and implement the right framework for their specific use cases.
AutoGen emerges as a powerful option for building multi-agent collaborative systems. The article details its core architecture, which enables multiple specialized agents to work together through structured communication protocols. Code examples demonstrate how to implement agent configurations, define conversation flows, and establish tool-use capabilities within the AutoGen paradigm. Particularly valuable is the coverage of AutoGen's human-in-the-loop patterns, which enable seamless collaboration between AI agents and human users when needed for complex decision-making or validation.
Semantic Kernel receives thorough treatment as a framework optimized for integrating AI capabilities with traditional software components. The article explores its plugin architecture, which allows developers to create modular, reusable components that combine AI and conventional code. Implementation examples show how to define semantic functions, create planning capabilities, and orchestrate complex workflows that leverage both LLM reasoning and deterministic code execution. The discussion extends to Semantic Kernel's memory and embedding features, which enable contextual understanding and knowledge retrieval across interactions.
Azure AI Agent Service stands out as a managed platform that simplifies agent deployment and operation. The article details its core components, including the agent builder interface, function calling capabilities, and deployment infrastructure. Code examples demonstrate how to define agent capabilities, implement custom tools, and deploy agents to production environments with appropriate monitoring and governance controls. The coverage includes practical guidance on when to choose this managed service approach versus the more flexible but lower-level frameworks.
Beyond framework details, the article provides valuable architectural patterns that apply across different implementation options. These include approaches to managing agent state, strategies for implementing tool use safely and effectively, and techniques for monitoring and debugging agent behavior during development and production. A detailed decision framework helps developers select the right option based on factors including development team expertise, deployment constraints, multi-agent requirements, and integration needs.
Frameworks & Tools Covered
- Microsoft AutoGen framework
- Semantic Kernel
- Azure AI Agent Service
- Function calling patterns
- Agent orchestration approaches
- Memory and embedding systems
- Tool integration techniques
- Prompt engineering for agents
- Azure OpenAI Service
- Agent state management
- Deployment patterns
- Monitoring and observability tools
Learning Outcomes
- Understand the architectural foundations and capabilities of leading agent frameworks
- Learn to select the appropriate framework based on specific use case requirements
- Master implementation patterns for multi-agent collaboration using AutoGen
- Develop modular, reusable agent capabilities with Semantic Kernel's plugin architecture
- Build and deploy production-ready agents using Azure AI Agent Service
- Implement effective memory systems that maintain context across interactions
- Create robust tool integration patterns that extend agent capabilities beyond conversation
Impact / Results
This article has equipped 3,800+ developers with practical knowledge for selecting and implementing AI agent frameworks. By providing clear comparisons, concrete code examples, and architectural guidance, it has accelerated the development process for teams building agent-based solutions across various domains.
The framework selection guidance has been particularly valuable, helping teams choose the right option for their specific requirements instead of defaulting to the most familiar or heavily marketed solution. Many readers have successfully implemented their first agent prototypes following the patterns described in the article, with several reporting significant time savings compared to figuring out these frameworks independently.
Community Engagement: 3,800 views on Microsoft Tech Community
Series Navigation
Series: AI Agents Series (Part 2 of 10)
Previous Article: Introduction to AI Agents (Part 1)
Next Article: Agent Principles (Part 3)