Dr. Jerry A. Smith's article introduces the Revenue Growth Multi-Agent System (RG-MAS), a novel framework for optimizing revenue growth. RG-MAS integrates psychological and sociological principles to understand human decision-making and collective behavior, creating a dynamic, adaptable system. Key components include a dynamic graph framework, specialized agents analyzing individual and group dynamics, and continuous feedback loops enabling the system to learn and improve. The system's antifragility allows it to thrive amidst market disruptions, offering applications across diverse industries. Further research will explore integrating emerging technologies and refining sociological models for broader applicability.

Medium Article: https://medium.com/@jsmith0475/leveraging-psychology-and-sociology-in-revenue-growth-a-multi-agent-antifragile-system-framework-8d1d6aef6d15

Leveraging Psychology and Sociology in Revenue Growth: A Multi-Agent, Antifragile System Framework

In this podcast, Apoorva Joshi, Senior AI Developer Advocate at MongoDB, discusses how to evaluate software applications that use the Large Language Models or LLMs and how to improve the performance of LLM based applications.

Read a transcript of this interview: https://bit.ly/3WEppT6

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Transcript

Apoorva Joshi on LLM Application Evaluation and Performance Improvements

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New tracks tagged #LangGraph