IMPROVING THE AUTOMATION AND AUTONOMY EFFICIENCY BY EMPLOYING LLM AS AGENTS

Large Language Models (LLMs) have proved to have spectacular capability in natural language understanding and generation but with growing value across a range of automation categories. They remain behind the current performance of state-of-the-art commercial models such as ChatGPT and GPT-4 even when utilized to handle difficult real-world problems. In order to work as fully fledged intelligent agents, LLMs will have to surpass language skills alone and perform complex task planning, long-term memories, context-independent reasoning, and have the facility for communication with outside tools. This paper presents one unified framework to improve the autonomy and efficacy of LLM-based agents. The core concept of our research is to design agent-dependent datasets and use the LLM as the core decision-making unit. By fine-tuning LLMs on agent-dependent datasets through supervised learning, especially in the scenario of smaller parameter models, we see a sharp reduction in hallucinations, format errors, and execution errors. We further improve agent performance with methods like multi-path reasoning and task decomposition that partition challenging tasks into less complex subtasks and thus increase reliability and flexibility. Our system is tested on five realistic automation tasks and shows significant improvements in task correctness, fault tolerance, and overall throughput. This article highlights the possibility of LLMs transforming when redesigned as autonomous agents, providing a future direction for intelligent scalable automation systems. They are able to learn to fit into new circumstances and minimize the need for constant human intervention.