Core Viewpoint - The article discusses the challenges faced by general-purpose AI agents, particularly in the context of market competition and user engagement, suggesting that many agents may be overshadowed by large models and specialized agents [4][6][12]. Group 1: Market Dynamics - General-purpose agents like Manus and Genspark are experiencing declining revenue and user engagement, indicating a lack of compelling applications that drive user loyalty and payment [6][20][23]. - Manus reported an annual recurring revenue (ARR) of $9.36 million in May, while Genspark reached $36 million ARR within 45 days of launch, showcasing the initial market potential [20]. - However, both products have seen significant drops in monthly recurring revenue (MRR) and user traffic, with Manus experiencing a 50% decline in MRR to $2.54 million in June [22][23]. Group 2: Competitive Landscape - The article highlights that general-purpose agents are struggling to compete with specialized agents that are tailored for specific tasks, leading to a loss of market share [15][17]. - The high subscription costs of general-purpose agents, combined with the increasing capabilities of foundational models, make them less attractive to users who can access similar functionalities at lower costs [12][28]. - Companies like Alibaba and ByteDance are focusing on developing their own agent platforms while promoting developer ecosystems, indicating a strategic shift towards enhancing their competitive edge [26][29]. Group 3: User Experience and Application - General-purpose agents have not yet identified "killer" applications that would encourage users to pay for their services, often focusing on tasks like PPT creation and report writing, which do not sufficiently engage users [24][32]. - The lack of integration with internal knowledge bases and business processes limits the effectiveness of general-purpose agents in enterprise settings, where accuracy and cost control are paramount [15][16]. - Current agents often struggle with complex tasks due to their reliance on multiple steps, leading to inconsistent output quality, which further diminishes user trust and engagement [33][34]. Group 4: Technological Innovations - Some developers are exploring innovations like reinforcement learning (RL) to enhance the capabilities of agents, aiming to transition from simple tools to more autonomous and adaptable systems [36][40]. - The article notes that advancements in model architecture, such as the introduction of linear attention mechanisms, are being leveraged to improve the performance of agents in handling large volumes of text [35][36]. - The potential for RL to significantly improve agent performance is highlighted, with recent tests showing substantial improvements in task handling capabilities [38][40].

Core Viewpoint - The general agent market is facing significant challenges, with companies like Manus experiencing declines in user engagement and revenue, indicating a lack of compelling use cases that drive sustained user loyalty and payment [2][9][11]. Group 1: Market Dynamics - Manus has relocated its headquarters to Singapore, laid off 80 employees, and abandoned its domestic version, reflecting a strategic shift rather than a failure in operations [2]. - The general agent market is being eroded by the overflow of model capabilities and competition from specialized agents, leading to a decline in revenue and user activity for general agents like Manus and Genspark [2][8]. - The market is witnessing a drop in monthly recurring revenue (MRR) for general agents, with Manus reporting a more than 50% decline in June [11]. Group 2: Product Performance - General agents have struggled to find killer applications that can attract and retain users, often being used for basic tasks like creating presentations or reports [2][9][11]. - The performance of general agents is hindered by their inability to match the precision of specialized agents in enterprise settings, leading to dissatisfaction among users [7][8]. - The pricing model of Manus, which relies on a points-based system, is seen as a barrier to user adoption compared to cheaper and more efficient model APIs [6][11]. Group 3: Technological Challenges - The rapid advancement of large models has made them increasingly agent-like, allowing users to directly utilize these models instead of relying on general agents [4][8]. - General agents often struggle with complex tasks due to their reliance on a step-by-step execution process, which can lead to errors and inconsistent output quality [16][19]. - Innovations in reinforcement learning (RL) are being explored to enhance the capabilities of agents, potentially allowing them to evolve from simple tools to more autonomous and adaptable systems [17][22]. Group 4: Competitive Landscape - The competitive landscape is shifting, with larger companies leveraging their resources to develop and promote their own agent products while also providing free services to attract users [12][13]. - The domestic market for general agents is becoming increasingly competitive, with major players like Baidu and ByteDance offering free testing and services, making it difficult for smaller companies to compete [12][13]. - The focus on deep research capabilities and multi-modal functionalities is becoming a common strategy among various agent developers to enhance their offerings [12][15].