Core Viewpoint - The article discusses the limitations of current AI Assistants, which primarily function as conversational agents, and emphasizes the need for the next generation of AI Assistants to evolve towards actionable intelligence, focusing on multi-modal interaction, real-time responsiveness, and cross-system execution capabilities [1]. Group 1: Limitations of Current AI Assistants - Current AI Assistants are still in the "dialogue" phase and are far from becoming true "universal agents" [2]. - The development challenges for AI Assistants are concentrated in four dimensions: intelligent planning and invocation, system latency and collaboration, interaction memory and anthropomorphism, and business models and implementation paths [2]. - Different technical paths are being explored, including general frameworks based on foundational models and scenario-specific closed-loop systems [2][4]. Group 2: Technical Pathways for AI Assistants - One core approach is to build a long-term, cyclical, and generalizable task framework that encompasses the entire process from goal understanding to task completion [3]. - The Manus framework exemplifies this approach by using a multi-step task planning and toolchain combination, where the LLM acts as a control center [4]. - MetaGPT emphasizes the need for components like code execution, memory management, and system calls to achieve cross-tool and cross-system scheduling capabilities [4]. Group 3: Scenario-Specific Approaches - Another technical path advocates for deep exploration within fixed scenarios, focusing on short-term task execution [4]. - Genspark, for instance, automates PPT generation by integrating multi-modal capabilities and deep reasoning modules [4]. - This scenario-specific approach is more stable and easier to deploy but struggles with non-structured tasks and domain transfer [4][5]. Group 4: Future Directions and Innovations - The Browser-Use approach aims to enhance agent capabilities by allowing them to interact with web interfaces like humans [6]. - Open Computer Agent can simulate mouse and keyboard operations for tasks like flight booking and web registration [6]. - No-Code Agent Builders are emerging as a recommended solution for the next generation of AI Assistants, enabling non-technical users to create and deploy workflows [7]. Group 5: System Optimization Challenges - AI Assistants must optimize for low-latency voice interaction, full-duplex voice capabilities, and the integration of hardware/system actions with application data and tool invocation [8].