大規模智能體：以感知為中心的持久型智能體架構

摘要

認知語言智能體透過為語言模型配備記憶、工具與決策程序，已取得顯著進展，使智能體得以在互動環境中進行推理與行動。現有框架大多將此類智能體視為用以解決使用者指定之有界限任務的系統。一個日益重要的目標，是使語言智能體能在長期運作的環境中提供持續性協助——在此類環境中，使用者需求、情境與服務程序既持續存在亦不斷變化——並使其在隨時間湧現的廣泛任務中持續發揮效用。然而，我們仍缺乏一套能夠表徵持續性AI智能體、統整既有研究並引導未來發展的框架。為此，我們提出了「以感知為中心的持續性智能體架構」(Perception-Centered Architecture for Persistent Agents, Pera)。Pera描述了一種圍繞感知與控制元件所組織的持續性智能體；這些元件持續感知來自片段式任務執行、內部脈絡及周遭環境變化的服務相關訊號，並運用這些訊號建構生命週期任務，進而驅動智能體服務程序的持續運作與調適。我們運用Pera回顧性地梳理近期研究、檢視一項詳細的案例研究，並為建構更具能力的持續性智能體提供前瞻性見解。正如軟體工程從「小型程式開發」邁向「大型程式開發」，Pera將語言智能體的演進，定位為一場朝向長期運作、具適應性智慧系統的架構性轉變。

English

Cognitive language agents have achieved substantial progress by equipping language models with memory, tools, and decision-making procedures, enabling agents to reason and act in interactive environments. Existing frameworks largely cast these agents as systems for solving user-specified, bounded tasks. An increasingly important goal is for language agents to provide persistent assistance in long-lived settings where user needs, context, and service procedures persist and change, and to remain useful across the broad range of tasks that arise over time. Yet we still lack a framework to characterize persistent AI agents, organize existing work, and guide future development. To this end, we propose a Perception-Centered Architecture for Persistent Agents (Pera). Pera describes a persistent agent organized around perception and control components that continually perceive service-relevant signals from episodic task executions, internal context, and changes in the surrounding environment, and use these signals to construct lifecycle tasks. These tasks drive the ongoing operation and adaptation of the agent's service procedures. We use Pera to retrospectively organize recent work, examine a detailed case study, and offer forward-looking insights for building more capable persistent agents. Just as software engineering moved from programming in the small to programming in the large, Pera frames the evolution of language agents as an analogous architectural transition toward long-lived, adaptive intelligence systems.