My Honest Take: One Year Into AI Agents

Hey everyone, Nina here from agntbox.com, and boy, do I have a fun one for you today. Remember last year when everyone was buzzing about AI agents finally being able to do actual work without constant hand-holding? Well, we’re a year on, and while the progress is undeniable, there’s still a chasm between the hype and the reality of building something truly autonomous and reliable.

I’ve spent the better part of the last month knee-deep in agent frameworks, trying to build a simple, self-correcting content summarizer. My goal? A bot that can take a long article, summarize it, check its own summary against some predefined criteria (like word count, key takeaway presence), and then revise it if it doesn’t meet the mark. Sounds straightforward, right? Famous last words.

Today, I want to talk about my experience with Microsoft AutoGen, specifically its GroupChat feature, and how it’s becoming my go-to for complex multi-agent workflows. But this isn’t just another “AutoGen is great!” piece. No, this is about getting real with its quirks, its power, and how I’m using it to tackle the thorny issue of self-correction in AI agents, especially when the initial output just isn’t quite there.

The Self-Correction Conundrum: Why It Matters

Let’s face it, even the best LLMs sometimes hallucinate, miss the mark, or just produce something that’s technically correct but utterly useless for your specific application. My content summarizer is a perfect example. I’d give it a 2000-word article and ask for a 200-word summary. Often, it would produce a 300-word summary, or miss a crucial point I wanted highlighted.

My initial approach was to just chain prompts: “Summarize this. Now, check if it’s 200 words. If not, revise.” This works for simple cases, but it quickly breaks down. The LLM might struggle to follow complex revision instructions, or it might get stuck in a loop. I needed a more dynamic, collaborative approach.

This is where AutoGen’s GroupChat really shines. Instead of one monolithic agent trying to do everything, you can set up a team of specialized agents, each with a specific role, to work together and critique each other’s output. Think of it like a mini-startup team, but with AI.

My AutoGen GroupChat Setup for Self-Correcting Summaries

My goal was to have a primary “Summarizer” agent, a “Critic” agent to evaluate the summary, and a “Reviser” agent to act on the critic’s feedback. I also needed a “Manager” agent to orchestrate the whole thing.

Agent Roles and Responsibilities

• Summarizer Agent: Its job is simple: take the article and produce an initial summary based on the provided instructions (e.g., “Summarize this article in 200 words, highlighting the main argument and key findings.”).
• Critic Agent: This agent is the stickler. It receives the original article and the summary. Its task is to evaluate the summary against specific criteria (word count, presence of key points, clarity, conciseness). If the summary meets the criteria, it gives a thumbs up. If not, it provides specific, actionable feedback for revision.
• Reviser Agent: The reviser takes the original article, the current summary, and the critic’s feedback. Its job is to incorporate the feedback and produce a revised summary.
• GroupChat Manager Agent: This is the maestro. It observes the conversation, determines who speaks next, and ensures the process moves towards a resolution. It also knows when to stop the conversation – for instance, once the Critic Agent approves a summary.

The Workflow in Action

Here’s how the dance typically goes:

1. The Manager gives the article to the Summarizer.
2. The Summarizer produces the first draft of the summary.
3. The Manager passes the summary (and original article) to the Critic.
4. The Critic evaluates.
5. If approved, the process stops.
6. If not approved, the Critic provides feedback to the Reviser.
7. The Reviser takes the feedback and the summary, and produces a revised summary.
8. The Manager passes the revised summary back to the Critic for another round of evaluation.
9. This loop continues until the Critic is satisfied or a maximum number of revision attempts is reached.

Getting Hands-On: A Snippet of My Setup

Let’s dive into some code. Setting up the agents themselves is pretty standard AutoGen, but the key is defining their system messages and the GroupChat configuration. This is where you bake in their personalities and rules.

import autogen

# Configuration for the LLM
config_list = autogen.config_list_from_json(
 "OAI_CONFIG_LIST",
 filter_dict={
 "model": ["gpt-4o", "gpt-4-turbo", "gpt-4"],
 },
)

llm_config = {"config_list": config_list, "temperature": 0.5}

# 1. The Summarizer Agent
summarizer_agent = autogen.AssistantAgent(
 name="Summarizer",
 llm_config=llm_config,
 system_message="""You are a professional content summarizer. Your goal is to create concise, accurate summaries of articles based on user instructions. 
 Focus on extracting the main argument, key findings, and conclusions. Be precise with word counts if specified.
 Your output should be the summary text ONLY, no conversational filler."""
)

# 2. The Critic Agent
critic_agent = autogen.AssistantAgent(
 name="Critic",
 llm_config=llm_config,
 system_message="""You are a meticulous content critic. Your role is to evaluate summaries against specific criteria.
 You will receive the original article and a proposed summary.
 Your criteria include:
 - Word count adherence (e.g., "The summary must be between 180 and 220 words.")
 - Presence of key points (e.g., "Does the summary mention the new drug's efficacy and side effects?")
 - Clarity, conciseness, and accuracy.
 If the summary meets ALL criteria, respond with "APPROVED".
 If it does NOT meet criteria, provide constructive feedback for revision, clearly stating what needs to be changed.
 For example: "The summary is 280 words, but should be 200. Please shorten it significantly. Also, it missed the point about the economic impact."
 Do NOT generate a new summary yourself. Only provide feedback or approve."""
)

# 3. The Reviser Agent
reviser_agent = autogen.AssistantAgent(
 name="Reviser",
 llm_config=llm_config,
 system_message="""You are an expert editor. You will receive an original article, a summary, and feedback from the Critic.
 Your task is to revise the summary based ONLY on the Critic's feedback.
 Do NOT introduce new information unless instructed. Focus on addressing the specific points raised by the Critic.
 Your output should be the revised summary text ONLY, no conversational filler."""
)

# 4. The GroupChat Manager
# This agent orchestrates the conversation.
groupchat = autogen.GroupChat(
 agents=[summarizer_agent, critic_agent, reviser_agent],
 messages=[],
 max_round=10, # Max rounds of conversation before giving up
 speaker_selection_method="auto", # Auto-selects the next speaker
 allow_repeat_speaker=False, # Prevents agents from speaking consecutively
)

manager = autogen.GroupChatManager(groupchat=groupchat, llm_config=llm_config)

# The user proxy agent initiates the conversation
user_proxy = autogen.UserProxyAgent(
 name="Admin",
 system_message="A human admin who initiates the summarization task and reviews the final output.",
 code_execution_config={"last_n_messages": 2, "work_dir": "output"},
 human_input_mode="NEVER", # Set to ALWAYS for manual intervention
 is_termination_msg=lambda x: x.get("content", "") and "APPROVED" in x.get("content", ""),
)

# Start the conversation
article_to_summarize = """
(Imagine a very long article text here, let's say about new advancements in quantum computing or climate change policy.
For brevity, I'll just put a placeholder. In a real scenario, this would be the full article.)
"""

summary_instructions = "Summarize the following article in 200 words, focusing on the main technological breakthroughs and potential societal impact."

user_proxy.initiate_chat(
 manager,
 message=f"Please summarize this article: \n\n{article_to_summarize}\n\nInstructions: {summary_instructions}"
)

A crucial part is the `is_termination_msg` in the `UserProxyAgent`. This tells the chat when it’s done. In my case, if the Critic agent says “APPROVED”, the conversation is over. Without this, the agents might just keep talking forever!

Real-World Challenges and My Workarounds

This setup isn’t a magic bullet. I ran into a few snags:

1. Getting Agents to Stick to Their Roles

Sometimes, the Critic would try to “fix” the summary itself instead of just giving feedback. Or the Reviser would add new information. This is where careful crafting of the `system_message` is absolutely essential. I iterated on these messages many times to make them very prescriptive about what each agent should and should not do.

• Workaround: Emphasize “ONLY” in instructions (e.g., “Your output should be the summary text ONLY”). Explicitly state “Do NOT generate a new summary yourself” for the Critic.

2. Feedback Loop Quality

If the Critic’s feedback wasn’t clear or specific enough, the Reviser would struggle. For example, “Make it better” is useless. “The summary is 280 words, but should be 200. Please shorten it significantly, focusing on combining related ideas” is much better.

• Workaround: I added examples of good feedback to the Critic’s system message and fine-tuned its prompt to encourage specific, actionable advice, often including examples like “For example: ‘The summary is X words, but should be Y. Please shorten it by Z words and address [specific missing point].'”

3. Max Rounds and Infinite Loops

Without a `max_round` limit, agents could theoretically get stuck in a loop, endlessly refining a summary that never quite satisfies the Critic. This is especially true if the initial summary is really bad or the instructions are ambiguous.

• Workaround: Set a reasonable `max_round` in `GroupChat`. For summarization, 5-10 rounds usually suffices. If it can’t be resolved by then, it often means the initial prompt or article is problematic, or the agents’ instructions aren’t clear enough.

4. Cost Considerations

More agents, more messages, more tokens. GroupChat conversations can become quite verbose, especially with multiple revision rounds. This directly impacts API costs.

• Workaround: Be mindful of the `max_round`. Optimize system messages to be concise yet effective. Consider using cheaper models for simpler agents if possible, though for critical roles like the Critic, I stick to GPT-4o for its reasoning abilities.

Beyond Summarization: Other Applications for Self-Correction

Once I got this self-correction pattern working for summarization, my mind started buzzing with other possibilities:

• Code Generation & Refinement: Imagine a “Coder” agent, a “Linter” agent (critic), and a “Debugger” agent (reviser) working together to write and fix code. The Linter identifies syntax errors or style violations, and the Debugger fixes them.
• Creative Writing & Editing: A “Writer” agent, an “Editor” agent (checking for plot holes, character consistency, grammar), and a “Reviser” agent.
• Data Analysis & Interpretation: An “Analyst” agent, a “Reviewer” agent (checking for statistical validity, correct interpretation of charts), and a “Corrector” agent.

The beauty is that the core pattern remains the same: a generator, a critic, and a reviser, all orchestrated by a manager. It’s a powerful paradigm for improving AI reliability and output quality.

Actionable Takeaways for Your Next AI Project

If you’re wrestling with getting consistent, high-quality output from your AI agents, especially when dealing with nuanced tasks or tasks requiring adherence to specific constraints, here’s what I recommend:

1. Break Down Complex Tasks: Don’t expect one agent to do everything perfectly. Divide the task into distinct roles (generation, evaluation, revision).
2. Leverage GroupChat: AutoGen’s GroupChat is excellent for this. It provides the framework for agents to collaborate and critique each other.
3. Craft System Messages Meticulously: This is arguably the most important part. Be extremely clear about each agent’s role, responsibilities, and what they should and should not do. Use words like “ONLY” and provide examples.
4. Define Clear Termination Conditions: Make sure your GroupChat knows when the task is complete to avoid endless loops and unnecessary costs. The `is_termination_msg` parameter is your friend.
5. Iterate on Feedback Mechanisms: The quality of the “Critic” agent’s feedback is paramount. Spend time refining its system message to ensure it provides specific, actionable guidance for revision.
6. Start Simple, Then Scale: Get a basic 3-agent (generator, critic, reviser) setup working for a simple task, then gradually add complexity or more specialized agents as needed.

AutoGen, particularly its GroupChat, has truly changed how I approach building more reliable and autonomous AI systems. It’s not just about getting an answer; it’s about getting the *right* answer, consistently. The journey from a basic prompt to a self-correcting agent team is definitely an investment, but the payoff in terms of output quality and reduced manual intervention is well worth it.

What are your experiences with multi-agent systems and self-correction? Hit me up in the comments or on social media. I’m always keen to hear how others are tackling these challenges!

🕒 Published: May 12, 2026