When Do We Need LLM-based AI Agents ?

Artificial intelligence (AI) has become an integral part of various aspects of our lives, often in the form of automation agents based on Large Language Models (LLMs). Advocates argue that these AI agents are the next step towards superior automation, acting as companions to increase our productivity and enrich our lives.

However, amidst this wave of enthusiasm for artificial intelligence (AI), questions persist regarding the reliability and practical applicability of these LLM-based autonomous agents.

LLMs are AI models that can understand and generate text similar to human text. Although they can mimic human reasoning, they lack the ability for critical and rigorous thinking, which raises doubts about their reliability. Despite these limitations, many people plan to use LLM-based agents for automation tasks. However, reliability and consistency are the foundations of automation, and LLM-based agents have yet to fully demonstrate these qualities.

An efficient automated system works flawlessly, performing the same operation with near-perfect reliability. However, when using LLM-based agents, it seems necessary to have constant human validation and monitoring. This approach may be suitable for some tasks, such as writing emails for human review and editing. However, for many other tasks, this may simply increase the human workload without bringing any obvious productivity gain.

Recently, another category of AI agents, called “autonomous AI agents”, has seen a significant increase. A notable example of this category is Auto-GPT. Released on March 30, 2023 by Toran Bruce Richards, Auto-GPT is an artificial intelligence agent designed to achieve goals by breaking them down into subtasks and autonomously using the Internet and other tools. Based on OpenAI’s GPT-4 or GPT-3.5 APIs, Auto-GPT is one of the first GPT-4 applications designed for autonomous task execution.

Unlike manually controlled systems such as ChatGPT, Auto-GPT sets its own goals in order to achieve a larger goal, eliminating the mandatory need for human intervention. However, Auto-GPT shares the underlying limitations of LLMs, such as a tendency to confabulatory “hallucinations” and difficulty staying focused on a task. Additionally, Auto-GPT often does not retain process history after task completion and has difficulty breaking down tasks and understanding overlapping goals in different issues.

Therefore, the question arises, “When should we use autonomous agents? Given their autonomous nature and the difficulty in controlling the quality of their output, these agents may not be reliable for many tasks. LLM-based autonomous agents should ideally be used in predictable and repetitive tasks that do not require critical thinking or decision making. They can be used for content generation, basic data analysis and customer service via chatbots.

Despite the potential of these LLM-based autonomous agents, it is essential to recognize their limitations. Until these AI models achieve a higher level of reliability and can demonstrate critical thinking and complex reasoning, human oversight will continue to be a crucial element in the use of these technologies. As AI continues to advance and evolve, our understanding of when and how best to deploy these promising tools will also increase. Thinking about this, how ready are you to adapt and evolve alongside these technological advancements?