Experiential Co-Learning of Software-Developing Agents

Through large language models (LLMs) have marked a engaging in interactive dialogues, each agent participates transformative shift across numerous domains in instructive and responsive conversations, (Vaswani et al., 2017; Brown et al., 2020; Bubeck collaboratively contributing to the achievement et al., 2023). Despite their impressive abilities, of a cohesive and automated solution for task when dealing with complex situations that extend completion. The development of a more adaptive beyond mere chatting, these models show certain and proactive approach to problem-solving by limitations inherent in their standalone capabilities these agents marks a significant leap in autonomy, (Richards, 2023). Recent research in autonomous going beyond the typical prompt-guided dynamic agents has significantly advanced LLMs in human-computer interactions (Yang et al., by integrating sophisticated features like contextsensitive 2023a) and substantially reducing dependence on memory (Park et al., 2023), multi-step human involvement (Li et al., 2023a; Qian et al., planning (Wei et al., 2022b), and strategic use of external 2023; Wu et al., 2023).