深度｜Agent 2025 趋势，编排工具向左，自主智能向右，智谱AutoGLM沉思如何押注？

Core Viewpoint - The article discusses the evolution and current state of AI Agent technology, highlighting significant advancements and challenges faced by the industry, particularly focusing on the emergence of new products like AutoGLM and the competitive landscape involving major players like OpenAI and Anthropic [2][4][22]. Group 1: Evolution and Current State of AI Agent Technology - AI Agent technology has progressed through three key stages, with notable breakthroughs occurring in early 2025, including OpenAI's DeepResearch and Anthropic's Claude 3.7 [4][8]. - The new generation of AI Agents demonstrates qualitative leaps in technical depth and application scenarios, particularly in vertical fields [2][4]. Group 2: Core Challenges Facing AI Agents - The three main challenges for AI Agents include execution reliability, with advanced systems achieving only 35.8% success rates on benchmarks, and GPT-4 at 14.9%, indicating inherent flaws in handling multi-step tasks [4][5]. - Generalization capability remains a significant shortcoming, as Agents struggle with cross-domain transfer, and improvements in one module can lead to overall performance declines [5]. - Efficiency and cost pressures are critical, with high costs associated with frequent API calls and significant delays in multi-round interactions, particularly in multi-Agent collaboration scenarios [5][6]. Group 3: Innovations by Global Tech Giants - OpenAI's DeepResearch project represents a significant innovation, utilizing reinforcement learning from self-play to autonomously execute tasks without external dependencies [11][12]. - Anthropic's Claude 3.7 Sonnet has achieved over a 20% improvement in software engineering benchmarks and introduced a hybrid reasoning model that balances speed and depth in task processing [15][14]. Group 4: China's Competitive Landscape - Chinese company Zhipu has made notable advancements with its AutoGLM system, which significantly enhances performance and cost-effectiveness compared to competitors, achieving an 8x speed improvement and reducing costs to 1/30 [22][25]. - Zhipu's approach integrates deep thinking with environmental interaction, allowing users to observe the model's reasoning process, marking a significant step in AI Agent development [22][23]. - The company aims to create an open and win-win Agentic technology ecosystem, focusing on low-cost solutions for scalable applications [25].