Core Insights - The article discusses the advancements in the Agent field, highlighting the impressive performance of large models in short-term tasks while revealing their limitations in long-term tasks. It emphasizes the need for a more scientific evaluation framework to assess the multi-modal understanding and complex problem-solving capabilities of these models [1][4]. Evaluation Framework - The introduction of the AgentIF-OneDay evaluation system aims to measure the ability of agents to solve complex tasks rather than just their knowledge base. This system explores the transition from OneHour to OneDay capabilities, revealing the true performance of mainstream agents in workflow execution, implicit inference, and iterative editing [1][6][10]. - The evaluation framework is designed to observe the evolution of industry technology routes and predict the upper limits of model capabilities, focusing on utility and economic value [6][10]. Task Complexity - Task complexity is defined not by the depth of knowledge or reasoning difficulty but by the human time investment required to complete a task, which correlates with its potential economic and utility value [6][7]. - The evolution of agent capabilities is expected to follow two main axes: scaling context (time dimension of tasks) and scaling domain (task type complexity). These axes determine the upper limits of task complexity that agents can handle [6][7]. Agent Capabilities - The AgentIF-OneDay framework tests agents' abilities to complete a full set of tasks within a day without human intervention, covering diverse domains such as life, learning, and work [10][11]. - Three primary task types are identified: Workflow Execution, Latent Instruction Inference, and Iterative Refinement, each representing different user interaction scenarios [11][14][15]. Testing Results - The evaluation of mainstream agent systems revealed that Manus, Genspark, and ChatGPT-Agent scored between 0.62 and 0.65 in overall task success rates, indicating similar capabilities across different systems [17][18]. - ChatGPT is identified as the best productivity tool for work, Manus as the best life assistant, and Genspark as the best study partner, showcasing the varying strengths of these agents in different domains [18][19]. Future Directions - The article anticipates that by 2026, agents will begin to challenge one-week human workloads, with the development of the OneWeek evaluation set already underway. This will involve more complex tasks and stricter rubric designs [22][23]. - The need for agents to possess active learning capabilities in real or semi-real environments is emphasized, suggesting that future advancements will rely on continuous learning and adaptation rather than static training methods [24][25].