Core Viewpoint - Manus, a new AI agent developed by the startup "Butterfly Effect," has gained significant attention for its capabilities in various tasks such as resume screening and stock analysis, but has recently withdrawn from the Chinese market, sparking controversy and discussion within the industry [1][2]. Strategic Focus of Manus - The co-founder, Ji Yichao, emphasized that Manus's decision to not develop its own underlying model was a strategic choice aimed at achieving Product-Market Fit (PMF), which is crucial for startup success [4][5]. - Initially, Ji Yichao considered self-developing a foundational model but realized that it would hinder the ability to meet market demands and validate PMF efficiently [5]. - The team recognized the risks associated with technological lock-in when relying on proprietary models, leading to the decision to build Manus based on cutting-edge models instead [5][6]. Investment in Context Engineering - Manus focuses on "Context Engineering," a critical concept in the application of large language models (LLMs), which involves optimizing input text to guide the model in generating desired outputs [8][9]. - Context Engineering aims to transition LLMs from general assistants to specialized experts that can integrate into various industry workflows, addressing the challenge of AI implementation in real-world scenarios [9]. Core Optimization Principles - Ji Yichao outlined six core optimization principles for Manus, including maintaining stable prompt prefixes, externalizing memory through a virtual file system, and dynamically updating task lists to enhance model performance [11][14]. - These principles are essential for the stability, efficiency, and scalability of the AI agent, which are critical for its commercial viability [12][14]. Market Withdrawal Reasons - The withdrawal from the Chinese market may be attributed to strategic considerations, including the inability to sustain product development across two markets and the pressure for commercial growth [15][16]. - Domestic users perceived Manus's pricing as high without clear differentiation from local competitors, impacting its conversion rates [16]. - The decision to focus on more commercially viable markets reflects the challenges faced by small to medium enterprises in the competitive AI landscape [16][17]. Industry Implications - Manus's experience signals to the industry that the core competitive advantage in AI agent commercialization may not lie solely in the underlying model but in how effectively a system is built around it to provide timely and relevant information [18][19]. - The ongoing trend of vertical AI agent startups and the emergence of new generation agents highlight the necessity for companies to create systems that integrate LLMs into professional workflows effectively [19].