Founder Park·2025-06-13 20:27Core Viewpoint - The emergence of Agents marks a significant shift in the AI landscape, transitioning from large models as mere tools to self-scheduling intelligent entities, creating new opportunities and challenges in the industry [1][2]. Group 1: The Rise of Agents - Agents have become the second major trend in the tech industry following large models, with a consensus forming around their potential [2]. - Despite the surge in consumer-facing products, many projects struggle to create a sustainable user value loop, often falling into the trap of applying new technology to old demands [2][3]. - The true barriers to the practical application of Agents lie in foundational infrastructure, including controlled operating environments, memory systems, context awareness, and tool invocation [2][3]. Group 2: Opportunities and Challenges - The conversation aims to uncover the real issues and opportunities within the Agent space, focusing on product forms, technical paths, business models, user experiences, and infrastructure construction [2]. - The transition from "Copilot" to "Agent" can be gradual, starting with user data collection and experience enhancement before evolving into fully automated solutions [9][19]. Group 3: Coding as a Key Area - Coding is viewed as a critical domain for achieving AGI, as it provides a clean, verifiable data environment conducive to reinforcement learning [24][25]. - The ability to code is seen as a universal skill that enables AI to build and create, potentially capturing a significant portion of the value in the large model industry [26][47]. Group 4: Evaluating Agents - A good Agent must create an environment that fosters a data feedback loop, with verifiable outcomes to guide optimization [27]. - Key metrics for assessing an Agent's effectiveness include task completion rates, cost efficiency, and user engagement metrics [30][31]. Group 5: Business Models and Market Trends - There is a shift from cost-based pricing to value-based pricing in the Agent market, with various models emerging, such as charging per action, workflow, or result [36][41]. - The trend of bottom-up adoption in organizations is becoming more prevalent, allowing products to gain traction without traditional top-down sales processes [35]. Group 6: Future of Human-Agent Collaboration - The concepts of "Human in the loop" and "Human on the loop" are explored to define the evolving relationship between humans and Agents, emphasizing the need for human oversight in critical decision-making [43][44]. - As Agents become more integrated into workflows, the nature of human interaction with these systems will evolve, presenting new opportunities for collaboration [45]. Group 7: Infrastructure and Technological Evolution - The foundational infrastructure for Agents includes secure execution environments, context management, and tool integration, which are essential for their effective operation [56][60]. - Future advancements in AI will likely focus on multi-agent systems, where different Agents collaborate to complete tasks, leading to a more interconnected digital ecosystem [53]. Group 8: The Role of Major Players - Major tech companies are beginning to differentiate their strategies in the Agent space, with some focusing on specific applications like coding while others leverage broader capabilities [54]. - The competition among giants like OpenAI, Anthropic, and Google is intensifying, with each company exploring unique paths to capitalize on the Agent trend [55].