代理型人工智慧的適應性

摘要

尖端的主體性人工智慧系統建立在基礎模型之上，這些模型可被調整用於規劃、推理及與外部工具互動，以執行日益複雜且專業化的任務。隨著此類系統在能力與應用範圍上的擴展，適應性機制已成為提升效能、可靠性與泛化能力的核心關鍵。本文將快速擴展的研究領域整合為系統性框架，涵蓋主體適應與工具適應兩大維度，並進一步將其分解為「工具執行信號觸發」與「主體輸出信號觸發」兩類主體適應模式，以及「主體無關」與「主體監督」兩類工具適應模式。我們證明此框架能清晰界定主體性AI適應策略的設計空間，明確揭示其權衡取捨，並為系統設計階段的策略選擇與切換提供實用指引。接著回顧各類別的代表性方法，剖析其優勢與局限，並指出關鍵的開放性挑戰與未來機遇。總體而言，本文旨在為建構更強大、高效且可靠的主體性AI系統的研究者與實踐者，提供概念基礎與實踐路線圖。

English

Cutting-edge agentic AI systems are built on foundation models that can be adapted to plan, reason, and interact with external tools to perform increasingly complex and specialized tasks. As these systems grow in capability and scope, adaptation becomes a central mechanism for improving performance, reliability, and generalization. In this paper, we unify the rapidly expanding research landscape into a systematic framework that spans both agent adaptations and tool adaptations. We further decompose these into tool-execution-signaled and agent-output-signaled forms of agent adaptation, as well as agent-agnostic and agent-supervised forms of tool adaptation. We demonstrate that this framework helps clarify the design space of adaptation strategies in agentic AI, makes their trade-offs explicit, and provides practical guidance for selecting or switching among strategies during system design. We then review the representative approaches in each category, analyze their strengths and limitations, and highlight key open challenges and future opportunities. Overall, this paper aims to offer a conceptual foundation and practical roadmap for researchers and practitioners seeking to build more capable, efficient, and reliable agentic AI systems.

代理型人工智慧的適應性

Adaptation of Agentic AI

摘要

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