Agents in the Large: Perception-Centered Architecture for Persistent Agents

2026-08-31

AI Summary

论文提出感知中心架构Pera，解决语言代理在持久环境中持续提供服务的问题，通过持续感知和适应环境变化来改进现有框架。

Abstract

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.

Problem

• Persistent Agents
• Long-lived Settings
• Adaptive Intelligence Systems
• Cognitive Language Agents

Innovation

• Persistent Agents
• Perception-Centered Architecture
• Adaptive Intelligence Systems