Do you build Agentic AI?

AI agents are autonomous entities powered by AI that can perform tasks, make decisions, and collaborate with other agents. Unlike traditional single-prompt LLM interactions, agents act as specialized workers with distinct roles, tools, and objectives.

Video: The Year of the AI Agent: Automation to Intelligence | Ulysses Maclaren | SSW User Group (1:30 hr)

Why Build Agentic Systems?

Automation: Handle complex, multi-step workflows autonomously.
Specialization: Assign agents to tasks they're optimized for (e.g., research, coding, analysis).
Scalability: Deploy an "army" of agents to tackle large projects.
Collaboration: Enable agents to communicate and share insights.

Leading Agent Frameworks Comparison

Feature	Autogen ⭐️	OpenAI Agents	LangGraph	n8n
Developed By	Microsoft	OpenAI	LangChain	n8n
Best For	Multi-agent conversations, flexible agent topologies	Production-ready pipelines with OpenAI models	Complex stateful workflows, cyclical execution patterns	No-code AI workflow automation
Learning Curve	Medium	Low	High	Low (visual interface)
Key Features	- Conversational agents - Event-driven - Group chat	- Agent SDK - Native handoffs - Built-in tracing - Guardrails	- Graph-based workflows - State management - Human-in-loop - Time-travel debugging	- Visual workflow editor - 400+ integrations - AI nodes
Integration	Python, various LLMs	Python/JS, OpenAI ecosystem	Python/JS, LangChain ecosystem	No-code interface, LangChain under the hood
Production Ready	Research-focused	Yes, built for production	Yes, enterprise-grade	Yes, for workflow automation

Note: Microsoft is planning to merge Semantic Kernel and AutoGen into a unified framework, combining enterprise Azure workflows with advanced agent capabilities.

When to Choose Each Framework

When choosing a framework for building agents, you need to be aware of the tradeoff between customisability and ease of use.

For example, n8n is a no-code solution that is only really useful for automating simple workflows, while frameworks like LangGraph offer deeper control but with a steeper learning curve.

The closer you get to the raw LLM API requests, the more customisable you can make it – however, this will often come at the cost of additional development effort.

OpenAI Agents SDK

The OpenAI Agents SDK is ideal when:

You require a straightforward, production-grade framework that's easy to learn
Your existing stack already includes OpenAI models and you want seamless integration
You need strong tracing and debugging capabilities built into the framework
You're building for production environments

LangGraph

Consider LangGraph when:

Your project involves intricate, recurring workflows requiring sophisticated state handling
You're developing systems with multiple interconnected agents
You have prior experience with the LangChain ecosystem
You benefit from visualizing your agent workflows graphically
You need enterprise-grade reliability

AutoGen ⭐️

AutoGen shines when:

Your application requires dynamic conversation flows between multiple agents
You're creating systems with diverse conversational agents working together
You value the backing of Microsoft's research division
You're working on research projects needing flexible agent interactions

n8n

Choose n8n if:

You need a no-code solution for building simple agent workflows
You want a visual, drag-and-drop interface
You have team members without coding experience who need to participate
You're creating marketing, sales, or design team prototypes

Language Limitations and Considerations

Most of the leading agent frameworks today, including AutoGen, LangGraph, and OpenAI Agents SDK, primarily use Python as their development language. This presents a challenge for organizations like SSW that predominantly use .NET and C#.

For C# developers, the most promising path forward is through Semantic Kernel. Microsoft is currently working on integrating AutoGen's capabilities into Semantic Kernel, which will allow .NET developers to leverage cutting-edge agent technology using C#. This integration is expected to bring the powerful multi-agent capabilities of AutoGen to the familiar and enterprise-ready .NET ecosystem.

Our recommendation: While waiting for the full Semantic Kernel integration, we recommend C# developers start experimenting with AutoGen in Python. The knowledge and patterns you learn will transfer directly to Semantic Kernel's implementation once it's complete, as Microsoft is directly incorporating AutoGen's architecture.

# Example AutoGen code that will be portable to Semantic Kernel in future
import autogen

# Define agents
assistant = autogen.AssistantAgent(
    name="Assistant",
    llm_config={"model": "gpt-4o"}
)

user_proxy = autogen.UserProxyAgent(
    name="User_Proxy",
    human_input_mode="NEVER"
)

# Start conversation
user_proxy.initiate_chat(
    assistant,
    message="How can I build a simple C# web app with an LLM backend?"
)

For a glimpse of how Semantic Kernel is already beginning to integrate with AutoGen, see this example:

import asyncio
import os

from autogen import ConversableAgent
from semantic_kernel.agents.autogen.autogen_conversable_agent import AutoGenConversableAgent

async def main():
    cathy = ConversableAgent(
        "cathy",
        system_message="Your name is Cathy and you are a part of a duo of comedians.",
        llm_config={
            "config_list": [
                {
                    "model": "gpt-4o-mini", 
                    "temperature": 0.9, 
                    "api_key": os.environ.get("OPENAI_API_KEY")
                }
            ]
        },
        human_input_mode="NEVER",  # Never ask for human input.
    )

    joe = ConversableAgent(
        "joe",
        system_message="Your name is Joe and you are a part of a duo of comedians.",
        llm_config={
            "config_list": [
                {
                    "model": "gpt-4o", 
                    "temperature": 0.7, 
                    "api_key": os.environ.get("OPENAI_API_KEY")
                }
            ]
        },
        human_input_mode="NEVER",  # Never ask for human input.
    )

    # Create the Semantic Kernel AutoGenAgent
    autogen_agent = AutoGenConversableAgent(conversable_agent=cathy)

    async for content in autogen_agent.invoke(
        recipient=joe, 
        message="Tell me a joke about NVDA and TESLA stock prices.", 
        max_turns=3
    ):
        print(f"# {content.role} - {content.name or '*'}: '{content.content}'")


if __name__ == "__main__":
    asyncio.run(main())

Figure: How AutoGen agents can be used in Semantic Kernel

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