This is a transcript of a conversation between Dibia (representing AutoGen) and participants from a company that has been using AutoGen for internal functionalities. The conversation covers various topics related to multi-agent systems, their applications, and the challenges they face.

**Introduction**

Dibia: Hi everyone, I'm Dibia from AutoGen. We're excited to be here today to discuss multi-agent systems and how they can help solve real-world problems.

Participant 1: Hi Dibia, thanks for joining us. We've been using AutoGen in-house for some prototypes and internal functionalities. We're interested in learning more about how you approach scalability, horizontal scaling, and other challenges that come with deploying multi-agent systems.

**Scalability**

Dibia: Ah, great question! At AutoGen, we're working hard to make our platform scalable, especially when it comes to horizontal scaling. Our new API version 0.4 is designed to be asynchronous, which allows us to take advantage of multi-threading and distribute tasks across multiple instances of the same agent.

Participant 1: That's great to hear. We've been wondering how to scale our AutoGen instances horizontally without losing knowledge shared between them. Do you have any advice on that?

Dibia: Yes, we do! Our new API has a scalability story that allows for horizontal scaling, and we're exploring ways to share knowledge across instances using techniques like data stores or message queues.

**Use Cases**

Participant 2: Can you give us some examples of real-world problems that multi-agent systems would be better at solving compared to single agents or humans?

Dibia: Absolutely. One example is back-office tasks, such as scanning emails for attachments and extracting data from them. Another example is software engineering tasks, like writing code or testing APIs. Finally, there are long-running tasks that require asynchronous processing, like filing taxes.

**Comparison with Competitors**

Participant 3: We're also interested in comparing AutoGen with other tools like LangChain. What's your take on the current state of the market and how you position yourself compared to competitors?

Dibia: I think the space is still early, and it's hard to tell which tool will win out. However, we're focused on iterating rapidly and improving our platform based on user feedback and industry trends.

**Conclusion**

Dibia: Thanks for having me today! It's been a great conversation, and I hope we've given you some insights into the world of multi-agent systems and how AutoGen can help solve real-world problems.