My LLM Agent Experiment: Real AI Impact at Work

Hey everyone, Sarah Chen here from agnthq.com, and boy do I have a story for you today. Remember last year when everyone was talking about LLMs and how they were going to change everything? Well, they did, but not in the way many expected. The real shift, the one that’s actually making a difference in how I work (and probably how you will too), isn’t just about big language models. It’s about the agents built on top of them.

I’ve been playing with these things for months now, trying to figure out which ones actually deliver on the hype. And let me tell you, there’s a lot of noise out there. But one platform has consistently impressed me with its practical utility, especially for someone like me who juggles content creation, research, and coding snippets: Microsoft AutoGen. Today, I’m not just reviewing AutoGen; I’m going to show you how I’m actually using it to make my life easier, focusing on its multi-agent capabilities for a very specific problem: content generation with embedded code examples.

Forget the generic “AutoGen is a framework for multi-agent conversations” spiel. We’re going to get our hands dirty. This isn’t just about building an agent; it’s about building a team of agents, each with a specific role, to tackle a complex task. Think of it like assembling a small, highly effective virtual team for a project.

The Problem: My Never-Ending Quest for Practical Code Snippets

As a tech blogger, my biggest headache isn’t just writing the article; it’s making sure the examples are good. I can write about AI agents all day, but if I can’t show you a quick, working piece of code that illustrates my point, what’s the use? Historically, this meant hours of manual testing, debugging, and often, realizing my initial idea was flawed. It’s a huge time sink.

I needed a system that could:

• Understand a high-level request for a code example.
• Generate the actual code.
• Test that code to ensure it works.
• Provide feedback if it doesn’t.
• Integrate that working code into a narrative.

That’s a lot for one LLM to handle, and often, when I tried to brute-force it with a single prompt, I’d get hallucinations, non-working code, or just generic platitudes. This is where AutoGen’s multi-agent approach shines.

My AutoGen Setup: The “Content Creator Crew”

I’ve set up a small “crew” of agents in AutoGen, each designed to handle a specific part of my content creation workflow. Here’s who’s on the team:

1. The “Writer” Agent (User_Proxy)

This is me, essentially. Or rather, it’s the agent that represents my input and receives the final output. It’s configured to allow human intervention, which is crucial for reviewing the final content and providing feedback on code. I don’t want to just blindly trust what the agents spit out; I need to guide them.

user_proxy = autogen.UserProxyAgent(
 name="Writer",
 human_input_mode="ALWAYS", # Important for guiding the process
 max_consecutive_auto_reply=10,
 is_termination_msg=lambda x: x.get("content", "").rstrip().endswith("TERMINATE"),
 code_execution_config={"work_dir": "agent_workspace"},
)

A quick note on human_input_mode="ALWAYS": This is key for me. It means after every round of agent conversation, AutoGen waits for my input. Sometimes I change it to “NEVER” if I’m confident in a workflow, but for complex creative tasks, “ALWAYS” keeps me in the loop.

2. The “Coder” Agent (Assistant)

This agent’s job is to write the actual code. It’s an assistant agent, meaning it doesn’t directly execute code, but it can propose code blocks. I’ve given it a system message that emphasizes clarity and practicality.

coder = autogen.AssistantAgent(
 name="Coder",
 llm_config={"config_list": [{"model": "gpt-4-turbo-preview"}]}, # Using a powerful model here
 system_message="You are a Python programmer. You write clear, concise, and functional Python code. When asked to provide a code example, generate only the code and any necessary import statements. Do not add explanations unless specifically asked. Focus on practical, runnable examples.",
)

I found that being super explicit in the system_message for the Coder agent reduced a lot of the “fluff” that LLMs often add, like long explanations before the code even starts. I just want the code, folks!

3. The “Tester” Agent (User_Proxy with Code Execution)

This is where the magic happens. The Tester agent is another UserProxyAgent, but its primary purpose is to receive code from the Coder, execute it, and report back the results. If there’s an error, it tells the Coder, and the Coder tries again. This feedback loop is invaluable.

tester = autogen.UserProxyAgent(
 name="Tester",
 human_input_mode="NEVER", # We don't need human input for testing, just execution
 max_consecutive_auto_reply=10,
 code_execution_config={"work_dir": "agent_workspace"},
 system_message="You are a Python code execution environment. You will receive Python code, execute it, and report the output. If there are errors, report them clearly.",
)

Setting human_input_mode="NEVER" for the Tester is important. We want it to be autonomous in its testing function. The code_execution_config points to a working directory, which is where all the generated scripts are saved and run.

4. The “Explainer” Agent (Assistant)

Once we have working code, the Explainer agent steps in. Its job is to take the functional code and explain it in plain language, suitable for a blog post. It also formats the output for easy integration.

explainer = autogen.AssistantAgent(
 name="Explainer",
 llm_config={"config_list": [{"model": "gpt-4-turbo-preview"}]},
 system_message="You are a technical content writer. You receive Python code and its output, and your task is to explain it clearly and concisely for a blog post audience. Provide a brief introduction to the code's purpose, a step-by-step explanation if needed, and wrap the code in a markdown block. Keep your explanations engaging and easy to understand.",
)

I found that giving the Explainer a clear mandate about its audience and output format really helps. It prevents it from just re-stating the code or being too verbose.

The Workflow: How They Talk to Each Other

Here’s how I orchestrate their conversation using AutoGen’s GroupChatManager:

groupchat = autogen.GroupChat(agents=[Writer, Coder, Tester, Explainer], messages=[], max_round=20)
manager = autogen.GroupChatManager(groupchat=groupchat, llm_config={"config_list": [{"model": "gpt-4-turbo-preview"}]})

# Initiate the conversation
Writer.initiate_chat(
 manager,
 message="I need a Python code example that demonstrates how to create a simple AutoGen multi-agent chat between two assistant agents. Make sure the code is runnable and includes a basic conversation.",
)

When I kick this off, here’s roughly what happens:

1. Writer (me) sends the initial request.
2. Manager directs the request to the Coder.
3. Coder generates a Python script for the multi-agent chat.
4. Manager then passes this code to the Tester.
5. Tester executes the code.
   • If there’s an error, Tester reports it back to the Coder, who then tries to fix it and generates new code. This loop continues until the code runs successfully.
   • If the code runs successfully, Tester reports the output.
6. Once working code and its output are confirmed, the Manager directs the conversation to the Explainer.
7. Explainer takes the working code and its output, and generates the explanatory text for my blog post, formatted with markdown code blocks.
8. Finally, the Writer (me) reviews the Explainer’s output and the entire conversation, providing a “TERMINATE” message if satisfied, or further instructions if not.

Practical Example: Generating an AutoGen Agent Chat Snippet

Let’s say I need a simple example for a blog post about basic AutoGen agent interaction. My prompt to the “Writer” (which is me initiating the chat) would be:

"I need a Python code example that demonstrates how to create a simple AutoGen multi-agent chat between two assistant agents. Make sure the code is runnable and includes a basic conversation."

Here’s a simplified version of what the agents might produce after a few rounds:

The Code (Generated by Coder, Tested by Tester)

import autogen

# Configuration for LLMs
config_list = [
 {
 "model": "gpt-4-turbo-preview",
 }
]

# Create the first assistant agent
agent1 = autogen.AssistantAgent(
 name="AgentA",
 llm_config={"config_list": config_list},
 system_message="You are AgentA. Your task is to greet AgentB and ask about the weather.",
)

# Create the second assistant agent
agent2 = autogen.AssistantAgent(
 name="AgentB",
 llm_config={"config_list": config_list},
 system_message="You are AgentB. Your task is to respond to AgentA's greeting and provide a simple weather update.",
)

# Create a user proxy agent to initiate the chat
user_proxy = autogen.UserProxyAgent(
 name="User_Proxy",
 human_input_mode="NEVER",
 max_consecutive_auto_reply=0, # This agent just kicks off the chat and doesn't reply
 is_termination_msg=lambda x: x.get("content", "").rstrip().endswith("TERMINATE"),
 code_execution_config={"work_dir": "agent_workspace"}, # Not strictly needed for this example, but good practice
)

# Start the conversation
user_proxy.initiate_chat(agent1, message="Start conversation with AgentB.")

The Explanation (Generated by Explainer)

This simple Python script demonstrates how to set up a basic conversation between two assistant agents using Microsoft AutoGen. We define two AssistantAgent instances, AgentA and AgentB, each with a distinct role defined in their system_message. AgentA is tasked with greeting and asking about the weather, while AgentB is set to respond with a weather update.

A UserProxyAgent, named User_Proxy, is used to kick off the dialogue. In this setup, User_Proxy acts as the orchestrator, initiating the chat with AgentA. Since human_input_mode is set to "NEVER" and max_consecutive_auto_reply to 0 for User_Proxy, it simply starts the conversation and then steps back, allowing AgentA and AgentB to interact based on their system messages and the flow of the conversation.

This pattern is fantastic for creating automated workflows where different AI entities collaborate to achieve a goal, like generating dynamic content or simulating interactions.

My Takeaways and What I’ve Learned

AutoGen, particularly its multi-agent capabilities, has been a significant boost for my productivity. It’s not about replacing me; it’s about giving me a highly capable, autonomous team to handle the grunt work and verification that used to eat up so much of my time. Here are my key takeaways:

1. Define Clear Roles: The more specific you are with each agent’s system_message, the better they perform. Ambiguity leads to generalist responses, which isn’t what we want in a specialized team. Think of it like a job description for each team member.
2. Iterative Refinement is Key: Don’t expect perfection on the first try. My agents, especially the Coder and Tester, went through many iterations of system messages and prompts until they started reliably producing what I needed. This is where the human_input_mode="ALWAYS" for my “Writer” agent is invaluable.
3. The Tester Agent is a Significant Shift: Seriously, having an agent that can execute code and provide immediate, objective feedback is transformative. It’s like having a dedicated QA engineer for every code snippet I generate. This drastically reduces the number of non-working examples I’d otherwise publish.
4. Manage the Conversation Flow: The GroupChatManager is powerful, but understanding how agents pass messages and who responds to whom is critical. Sometimes, I explicitly direct agents (e.g., “Coder, please respond to Tester’s feedback”) if the manager gets confused.
5. LLM Choice Matters: While AutoGen works with various LLMs, I’ve found that more capable models like GPT-4-Turbo-Preview produce significantly better results, especially for code generation and complex explanations. It’s worth the extra cost for critical tasks.

AutoGen isn’t just a platform; it’s a new way of thinking about how AI can assist in complex tasks. It moves beyond single-turn prompts to orchestrate sophisticated workflows. For content creators, developers, or anyone needing to generate and verify technical examples, this multi-agent approach is, in my honest opinion, one of the most practical and impactful applications of AI agents I’ve seen yet.

So, if you’re drowning in the specifics of code examples for your content or projects, give AutoGen’s multi-agent system a try. It might just be the virtual team you didn’t know you needed. Let me know in the comments if you’ve tried similar setups or have any questions!

Until next time, keep building and exploring!

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🕒 Published: March 17, 2026