The content discusses the impracticality of using AutoGen and similar multi-agent frameworks with the current generation of LLMs. The author shares their conclusion after building several AutoGen prototypes and explains that while it is a useful tool for research, learning, and hobby projects, it should not be used in customer-facing apps. The author raises concerns about using AutoGen with the current generation of LLMs and provides an experiment to demonstrate these concerns.
The experiment involves setting up a multi-agent framework with AutoGen for multi-hop question answering. The author explains that multi-hop question answering involves answering complex questions that require gathering and synthesizing information from multiple data sources or pieces of evidence. The multi-agent workflow consists of four autonomous agents: Planning Agent, Web Search Tool, Integration Agent, and Reporting Agent. The author explains the role of each agent in the multi-agent workflow and how they work together to answer complex questions.
The author highlights the inadequacy of basic retrieval augmented generation (RAG) and explains that a multi-agent approach might provide more flexibility. However, they point out that there are limitations to using AutoGen and similar multi-agent frameworks with the current generation of LLMs. The experiment serves as a demonstration of these limitations and raises concerns about the practicality of using AutoGen in real-world applications. Overall, the content provides insights into the challenges and limitations of using AutoGen and similar frameworks with the current generation of LLMs.
Source link: https://pub.aimind.so/autogen-isnt-practical-for-real-world-applications-yet-5b8c6dc97641?source=rss——artificial_intelligence-5