Agent Instructions for AI Trajectory Fine-Tuning

This document is a research article that discusses the challenges faced by artificial intelligence (AI) agents in achieving reliable outcomes in real-world scenarios, particularly focusing on the issue of trajectory fine-tuning. The paper introduces a concept called 'Agent Instructions' within the SuperAGI framework, which serves as a guidebook for AI agents during their provisioning phase. These instructions enhance the agents' ability to learn from past experiences and adjust their paths accordingly, thereby improving efficiency and reducing reliance on first principles thinking. The article also proposes a second version of Agent Instructions that utilizes Language Models (LLMs) for recursive trajectory fine-tuning. In this model, agents perform self-analysis post-execution to generate optimized instruction sets for subsequent runs, creating a self-improvement loop. The paper outlines the theoretical foundations, practical implications, and future research directions related to this innovative approach, aiming to bridge the gap between the potential and actual performance of AI agents.