📄 中文摘要
AI 代理的崛起标志着从简单的自动化向更复杂的认知协同的转变。这些代理不仅仅是执行预定义任务的脚本，而是能够进行推理、学习和适应的自主实体。要释放它们的潜力，关键在于为认知协同进行架构设计。与基于大语言模型（LLM）的代理的主要交互方式是提示工程，精确的提示设计至关重要。通过逆向工程 LLM 的内部表示，可以实现更高效的交互和更好的结果。提示的质量直接影响代理的表现，因此在设计时需要关注语义层面的精确性。
你的 AI 代理将更有效：为自主协作的未来优化
📄 English Summary
Your AI Agents Will Work Better: Optimizing for a Future of Autonomous Collaboration
The rise of AI agents signifies a shift from simple automation to more complex cognitive synergy. These agents are not just scripts executing predefined tasks; they are autonomous entities capable of reasoning, learning, and adapting. Unlocking their potential hinges on architecting for cognitive synergy. The primary interface with LLM-powered agents is prompt engineering, where the quality of prompts is crucial. By reverse engineering the internal representations of LLMs, more efficient interactions and better outcomes can be achieved. The quality of prompts directly impacts the performance of agents, necessitating a focus on precision at the semantic level during design.