AI Agents: Exploring Agentic Frameworks - Part 2

Brass Contributor

Mar 11, 2025

Hi everyone, Shivam Goyal here again! In the first post, we introduced the fundamental concepts of AI agents. Now, we'll get practical and explore Microsoft's agentic frameworks: AutoGen, Semantic Kernel, and Azure AI Agent Service. This post will equip you to choose the right tool for your AI agent projects.

Why AI Agent Frameworks?

AI agent frameworks take AI further than traditional frameworks by enabling dynamic interactions between agents and their environment. They offer:

Agent Collaboration and Coordination: Build sophisticated multi-agent systems where agents can work together seamlessly, sharing information and coordinating actions to achieve complex goals.
Task Automation and Management: Streamline and automate intricate workflows, distributing tasks efficiently among multiple agents and managing their execution.
Contextual Understanding and Adaptation: Empower agents to understand the context of their environment and adapt their behavior accordingly, leading to more robust and intelligent systems.

Rapid Prototyping and Iteration

These frameworks accelerate the development lifecycle through:

Modular Components: Leverage pre-built modules like AI connectors, prompt templates, and memory management to assemble functional prototypes and iterate rapidly on your designs quickly.
Collaborative Tools: Design and test multi-agent workflows with ease, enabling you to experiment with different agent roles, communication strategies, and coordination mechanisms.
Real-Time Learning: Implement feedback loops that allow agents to learn from their interactions and adjust their behavior dynamically, leading to continuous improvement and more intelligent performance over time.

Comparing Frameworks

AutoGen: An open-source, experimentation-focused framework ideal for prototyping sophisticated multi-agent systems and exploring advanced design patterns. Key features include:

Agents: Autonomous entities capable of communication, state management, and performing actions.
Multi-agent Systems: Robust frameworks for building systems with multiple interacting agents.
Agent Runtime: Standalone and distributed runtime environments for managing the lifecycle of agents.

# Example of registering an agent with AutoGen runtime = SingleThreadedAgentRuntime() await MyAgent.register(runtime, "my_agent", lambda: MyAgent()) # Register your custom agent class

Semantic Kernel: A production-ready SDK providing AI and memory connectors, a planner for orchestrating actions, and a robust agent framework. Key components:

AI Connectors: Seamlessly integrate with various AI services, providing a unified interface for accessing their capabilities.
Plugins: Encapsulate reusable functions (prompt functions and native functions) that agents can utilize to perform specific tasks.
Planner: Orchestrate complex execution plans based on user input and desired outcomes.
Memory: Manage context and knowledge, allowing agents to retain information and learn from past interactions.

// Example of a Semantic Kernel plugin function [SKFunction, Description("Retrieve content from local file")] public async Task<string> RetrieveLocalFile(string fileName, int maxSize = 5000) { string content = await File.ReadAllTextAsync(fileName); return content.Length <= maxSize ? content : content.Substring(0, maxSize); }

Azure AI Agent Service: A platform service within Azure Foundry specifically designed for building, deploying, and managing AI agents at scale in a secure and enterprise-ready environment. Key features:

Flexible Model Support: Utilize a wide range of LLMs, including open-source models, offering flexibility and choice in your agent design.
Tool Calling: Seamlessly integrate with external tools and services, expanding the capabilities of your agents and enabling them to perform diverse actions.
Multi-Agent Orchestration: Supports integration with other agent frameworks like AutoGen and Semantic Kernel, providing flexibility and extensibility.

# Example of creating an Azure AI Agent agent = project_client.agents.create_agent( model="gpt-4o-mini", name="my-agent", instructions="You are a helpful agent", # ... other configuration parameters ... )

Choosing the Right Framework

Experimentation and Rapid Prototyping: AutoGen is ideal for exploring different multi-agent architectures and design patterns.
Building Robust, Production-Ready Applications: Semantic Kernel provides the tools and structure needed for enterprise-grade agent development.
Simplified Deployment and Management on Azure: Azure AI Agent Service streamlines the deployment and scaling of your agents within the Azure cloud environment.

Consider developing your agent application using Semantic Kernel and then deploying it using Azure AI Agent Service for a balanced approach that combines development flexibility with robust deployment capabilities.

Integrating with Azure

Azure AI Agent Service is designed for seamless integration with the broader Azure ecosystem. AutoGen and Semantic Kernel can also integrate with Azure services, though this may require direct code calls or the use of Azure SDKs.