Core Insights - The article discusses the current limitations and challenges faced by AI agent technologies, particularly in comparison to traditional task bots, highlighting that the user experience has not significantly improved over the past decade [1][2]. Group 1: Planning Challenges - The planning phase is time-consuming, and as the number of tools increases, the accuracy of turbo models declines, necessitating the use of flagship models, which further increases latency [2][5]. - The quality of planning is insufficient; the workflows generated by models are less effective than those designed by humans, particularly in complex scenarios [2][8]. - The core issue with slow planning is the underestimation of the costs associated with tool discovery and parameter alignment, leading to a complex optimization problem when dynamically selecting tools [5][21]. Group 2: Reflection Issues - Reflection processes can lead to self-reinforcing cycles of inefficiency due to a lack of fine-grained computable signals and clear stopping conditions [3][15]. - Current models rely on weak feedback mechanisms, which can result in reinforcing incorrect assumptions rather than correcting errors [15][20]. - Proposed solutions include structured reflection processes that allow models to learn from mistakes and improve their performance through reinforcement learning [18][20]. Group 3: Engineering Solutions - Suggestions for improving planning quality include decomposing plans into milestones and local prompts, which can enhance stability and reusability [8][10]. - Implementing parallel execution of tasks can reduce overall processing time, with evidence showing a 20% reduction in time for non-dependent tool calls [6][21]. - The introduction of routing strategies can streamline task execution by directing simpler tasks to specialized executors, reserving complex planning for stronger reasoning models [6][21]. Group 4: Future Directions - The article emphasizes the importance of combining reinforcement learning with agent models to enhance their reasoning and execution capabilities, indicating a trend towards end-to-end learning approaches [20][21]. - The potential for AI agents to become valuable applications of large language models (LLMs) in real-world scenarios is highlighted, with ongoing improvements expected as models evolve [21].