Agent开发中的坑与解_殷杰 百度智能云高级产品经理

Core Insights - The report discusses the challenges and solutions in the development of Agents, highlighting the contrast between ideal expectations and real-world difficulties [1][2]. Pre-Launch Phase - Common pitfalls include unclear goals, neglecting data tools, lack of valuable business scenarios, and insufficient ROI evaluation [9][10]. - Solutions involve focusing on small, pain-point-driven topics, ensuring data accessibility and quality, clarifying customer needs, and setting quantifiable ROI metrics [9][10][11]. Development Phase - Issues faced during development include model selection difficulties, improper usage, cost overruns, vague prompts, chaotic knowledge management, and weak security measures [2][20]. - Strategies to address these include utilizing platforms like Baidu's Qianfan for model selection, designing clear prompts akin to PRD writing, optimizing knowledge management, and establishing a robust security framework [2][20][26]. Post-Launch Phase - Common problems after launch include lack of monitoring alerts, inadequate scaling and disaster recovery mechanisms, and insufficient user feedback systems [2][20]. - Recommendations include identifying resource dependencies, configuring redundant capacities, establishing comprehensive logging and monitoring systems, and enhancing user feedback mechanisms for continuous optimization [2][20]. Overall Development Approach - The development of Agents should adhere to a multi-faceted principle, balancing key elements to ensure high availability and continuous improvement, ultimately creating intelligent agents that meet user needs [1][2].