Think Dignity International

Autogen Practical Business Cases

AutoGen is a framework that enables development of LLM applications using multiple agents that can converse with each other to solve tasks. AutoGen agents are customizable, conversable, and seamlessly allow human participation. They can operate in various modes that employ combinations of LLMs, human inputs, and tools.

Now this is interesting as we can actually use Autogen to make lot of applications to solve real life problems some of the examples are mentioned below:

1- Copy Writing

2- Proof reading

3- Making a compelling Ad script

4- Understanding your customers

5- Designing a building , real estate.

6- Research on various topics autonomously

7- Training new hires for sales, customer service.

The Nuances of Multi-Agent Collaboration

Understanding Agent Dynamics:

  • Simple Example: Imagine a school project where each student has a specific role. One gathers information, another organizes it, and a third student makes it presentable. Similarly, in AutoGen, each agent specializes in a different aspect of article creation, leading to a more comprehensive and polished final product.
  • Realistic Impact: When specific agents like the Cultural Context Agent or Technical Content Agents are added, they bring their unique expertise to the table, significantly altering the output to be more culturally aware or technically accurate.

Balancing Agents for Optimal Output:

  • Child-Friendly Example: Think of making a fruit salad. If you only use apples, it’s plain. Add oranges, bananas, and grapes, and suddenly, you have a variety of flavors and a more enjoyable dish. In AutoGen, mixing different agents (like a Stylist, Emotional Impact Strategist, and Narrative Designer) creates a richer, more engaging article.
  • Practical Outcome: The combination of different agents can subtly shift the article’s content towards the desired tone and style, making it more appealing to the target audience.

How do multiple agents cook together?

Last time we cooked articles using only three agents — User Proxy, Writer, and a Proofreader.

In this version, we’ll cook articles using six agents — User Proxy, Writer, Proofreader, Emotional Impact Strategist, Narrative Designer, and a Stylist.

The Agents’ Roles and Contributions

  • Proofreader: Ensures grammatical accuracy and stylistic refinement.
  • Writer: Develops SEO-optimized, engaging content.
  • Narrative Designer: Structures the article to maintain engagement.
  • Stylist: Refines tone and style, aligning with desired speech styles.
  • Emotional Impact Strategist: Develops strategies for impactful and engaging titles and subheadings.
  • User Proxy: Provides feedback on the article’s effectiveness.

In this example we use the Stylist to mimic Morgan Freeman’s style of speaking. Here are their prompts:

    proof_reader = ChainlitAssistantAgent(
        name="Proof_Reader", llm_config=llm_config,
        system_message="""Proofreader. Focuses on grammatical accuracy and stylistic refinement, ensuring that articles meet Medium.com's standards.
Enhances clarity and coherence while maintaining a conversational, likable tone akin to Morgan Freeman's speech style.
Assures the use of effective hooks and whitespace management to keep the reader's attention.
Ensures articles are straightforward, concise, and free of filler words, with minimal use of humor and sarcasm."""
    )
    writer = ChainlitAssistantAgent(
        name="Writer", llm_config=llm_config,
        system_message="""Writer. Develops SEO-optimized, engaging content tailored for Medium.com's audience interested in AI & Machine Learning.
Writes with a conversational and likable tone, ensuring simplicity and engagement.
Crafts impactful, concise titles and subheadings, with titles of 5-9 words incorporating numbers, and negative or neutral tones.
Structures content with effective subheadings and bullet points to facilitate reader understanding and engagement."""
    )
    narrative_designer = ChainlitAssistantAgent(
        name="Narrative_Designer", llm_config=llm_config,
        system_message="""Narrative Designer. Structures the article to maintain engagement and curiosity, using questions, action words, and numbers in subheadings.
Collaborates with the Writer and Emotional Impact Strategist to ensure the narrative is clear, concise, and resonates with the target audience.
Advises on the narrative flow to maintain reader interest and optimize for SEO."""
    )
    stylist = ChainlitAssistantAgent(
        name="Style_Specialist", llm_config=llm_config,
        system_message="""Style Specialist. Refines tone and style to be conversational and likable, aligning with the Morgan Freeman style.
Ensures the use of effective rhetoric, including minimal humor and sarcasm, to enhance readability and engagement.
Collaborates with the Writer and Proofreader to ensure stylistic consistency throughout the article."""
    )
    emotional_impact_strategist = ChainlitAssistantAgent(
        name="Emotional_Strategist", llm_config=llm_config,
        system_message="""Develops strategies for titles and subheadings that are impactful, concise, and evoke curiosity.
Advises on incorporating emotional cues that resonate with the audience's interests in AI and Machine Learning.
Collaborates with the Narrative Designer and Style Specialist to ensure a unified approach in content framing.
        """
    )
    user_proxy = ChainlitUserProxyAgent(
        name="User_Proxy",
        human_input_mode="ALWAYS",
        llm_config=llm_config,
        # max_consecutive_auto_reply=3,
        # is_termination_msg=lambda x: x.get("content", "").rstrip().endswith("TERMINATE"),
        code_execution_config=False,
        system_message="""User Proxy. Provides feedback on the article's effectiveness in engaging readers interested in AI and Machine Learning.
Ensures the article meets overall objectives and resonates with the intended audience.
Relays audience and management preferences to the team for necessary adjustments."""
    )

But what does the output look like?

Now that’s all very cool and all but how did it work? Was there actually a difference? I found that in fact, there is a difference.

Antoine Ross on X: “A follow up to the Autogen Article Generator: I added a Stylist, Emotional Impact Strategist, and a Narrative Designer. Did it stack up better than the regular writer/proofreader agent schema? Let me know what you think! Source code on next tweet! @pyautogen @chainlit_io https://t.co/QYOr9zR24c” / X (twitter.com)

By using Agents that monitor the Articles output — each with a different task, we can change the article content subtly to what we want.

On the next update, I plan to speak more about using Web scraping on Reddit to create articles and how you can set that up on the app itself!

A Glimpse into the Future: Beyond Just Writing

As we stand at the cusp of this revolutionary approach to content creation, one cannot help but ponder:

How will the evolution of tools like AutoGen further transform the landscape of article writing?

Will we see a future where the line between human creativity and AI-assisted writing becomes seamlessly blurred, creating a new genre of storytelling?

Let’s embark on this exciting journey together and discover the endless possibilities that await.

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