Autogen: Building Conversational Agents

While CrewAI is great for tasks, Microsoft Autogen is the king of conversational agents that can "Talk and Code" their way to a solution. In this lesson, we learn how to architect an Autogen swarm where agents iterate on each other's work autonomously.

🏗️ The Autogen Conversation Logic

1. The User Proxy: Acts as the bridge between you and the swarm.
2. The Assistant Agent: The primary worker (e.g., The Coder).
3. The Critic Agent: Reviews the output and provides feedback loops until the goal is met.

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🛠️ Technical Snippet: A Basic Autogen Swarm

import autogen

config_list = [{"model": "gpt-4", "api_key": "..."}]

assistant = autogen.AssistantAgent("assistant", llm_config={"config_list": config_list})
user_proxy = autogen.UserProxyAgent("user_proxy", code_execution_config={"work_dir": "coding"})

user_proxy.initiate_chat(assistant, message="Write a Python script to scrape 10 leads from LinkedIn.")

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🔍 Nuance: Code Execution Safety

Autogen agents can execute the code they write. This is powerful but dangerous. A professional architect always runs Autogen in a Docker Container to ensure the agents cannot accidentally delete files on the host machine.

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⚡ Practice Lab: The Feedback Loop

1. Setup: Create an Assistant and a Critic agent.
2. Task: Ask them to "Write a viral headline."
3. Loop: Watch the Critic reject the first 3 headlines and force the Assistant to improve the "Emotional Hook."
4. Verify: Note how the final output is 10x better than the first attempt.

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📝 Homework: The Self-Correcting Coder

Design an Autogen system that: (1) Writes a Python function (2) Runs a test on it (3) If the test fails, the agent must read the error and fix the code autonomously.