Core Viewpoint - The article discusses the pivotal moment in the AI agent landscape with the release of OpenAI's ChatGPT Agent, which integrates deep research and execution tools, prompting a reevaluation of technology paths in general AI agents [4][5]. Group 1: Main Architectures of AI Agents - The primary architectures for AI agents include browser-based agents, sandbox environments, and workflow-integrated agents, each with distinct advantages and limitations [16][24]. - Browser-based agents, like OpenAI's, are versatile but slow and resource-intensive, while sandbox agents can execute scripts offline but lack internet access [18][22]. - Workflow-integrated agents, such as those from Zapier and UIPath, rely on third-party service integrations, ensuring reliable service delivery but limiting user customization [22][24]. Group 2: User Experience and Performance - User experience varies significantly among agents like Pokee, Genspark, Manus, and OpenAI's ChatGPT Agent, with Pokee being the fastest, achieving speeds 4-10 times faster than competitors [33][35]. - Manus and ChatGPT Agent share a common limitation of being slow due to their reliance on browser navigation, which can take over 30 minutes for task completion [29][31]. - Genspark has shifted towards creating templates for specific tasks, which enhances speed but reduces its versatility as a general agent [31]. Group 3: Business Models and Market Dynamics - The article highlights a shift in business models from consumer-focused (ToC) to professional-oriented (ToB) applications, with a focus on repetitive tasks that professionals are likely to automate [41][44]. - The integration of agents is expected to disrupt traditional web traffic, as users may rely more on agents for tasks rather than navigating through browsers [46][48]. - The future of advertising is anticipated to evolve, with agents potentially compensating content creators directly, altering the traditional revenue models based on traffic and views [53][55]. Group 4: Future Implications for the Internet - The rise of AI agents is predicted to transform the internet's structure, with agents serving as primary interfaces for users, potentially leading to a decline in traditional web traffic [48][49]. - The article suggests that the recommendation systems may face significant changes, moving from ranking-based algorithms to more interactive, experience-driven models [59][61].

Core Insights - OpenAI has officially launched its ChatGPT Agent, marking a significant moment in the evolution of general-purpose AI agents, integrating deep research and execution tools, although it still faces challenges such as slow speed and lack of personalization [1][4][36] - The architecture of ChatGPT Agent is fundamentally a combination of a browser and a sandbox virtual machine, which contrasts with other agents like Manus and Genspark, highlighting different technical paths and capabilities [1][4][12] Architecture Comparison - The main types of AI agents currently available include browser-based agents, sandbox agents, and workflow-integrated agents, each with distinct advantages and limitations [12][26] - OpenAI's browser-based product is noted for its strong capabilities, achieving over 50% on the Browsing Camp benchmark, while competitors like Perplexity and Genspark have lower scores [4][6] - Browser-based agents are versatile but slow, while sandbox agents can execute tasks efficiently but often lack internet access [14][17] User Experience and Performance - User experience varies significantly among agents like Pokee, Genspark, Manus, and OpenAI's ChatGPT Agent, with Pokee being the fastest, potentially 4-10 times quicker than its competitors [36][40] - Manus and ChatGPT Agent share a common drawback of slow performance due to their reliance on browser navigation, with tasks taking upwards of 30 minutes [28][31] - Genspark has shifted towards a template-based approach, which may limit its general-purpose capabilities but improves speed and efficiency [34][33] Market Dynamics and Future Trends - The rise of AI agents is expected to transform internet traffic distribution, potentially reducing reliance on traditional web browsing and search engines [52][56] - Companies are increasingly motivated to open API interfaces to facilitate the integration of AI agents, which could lead to a decline in direct web traffic to traditional sites [52][58] - The advertising landscape is anticipated to evolve, with agents potentially compensating content creators directly, altering the traditional revenue models [64][66]