Who does the work, the model or the software? (www.thepythoncodingstack.com)

In a recent discussion on the evolving role of AI agents, the conversation delved into the interaction between large language models (LLMs), the software that connects them, and the tools that execute tasks. The dialogue highlighted that while an LLM generates requests—such as organizing photographs—it is the surrounding software, often referred to as a “harness,” that actually performs actions like accessing files or reading metadata. This distinction is crucial, as it underscores the limitations of LLMs, which, despite their ability to process language, cannot directly manipulate or access data on a user's computer without the coordinating software. This exploration is significant for the AI/ML community as it clarifies the operational dynamics within AI systems. Understanding that the "brain" (the LLM) proposes actions while the "muscles" (tools) carry them out, facilitated by the "coordinator" (harness), provides valuable insight into designing more effective AI applications. Furthermore, the conversation illuminates how interactions unfold in an "agent loop," where each step informs the next. As AI becomes more integrated into practical applications, such insights will be vital for developing systems that can seamlessly manage complex tasks while maintaining adherence to user permissions and operational constraints.