智能体人工智能的适应性

摘要

前沿的智能体AI系统建立在基础模型之上，这些模型能够通过适配实现规划、推理和外部工具交互，以执行日益复杂和专门化的任务。随着此类系统能力与适用范围的扩展，适配机制已成为提升性能、可靠性和泛化能力的核心手段。本文将这些快速发展的研究方向系统化地整合为涵盖智能体适配与工具适配的统一框架，并将其进一步分解为工具执行信号驱动与智能体输出信号驱动的智能体适配模式，以及智能体无关与智能体监督的工具适配模式。我们证明该框架有助于厘清智能体AI适配策略的设计空间，明确其权衡关系，并为系统设计过程中的策略选择与切换提供实践指导。随后我们逐类评述代表性方法，剖析其优势与局限，并指出关键开放挑战与未来机遇。总体而言，本文旨在为构建更强大、高效、可靠的智能体AI系统提供概念基础与实践路线图。

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.

智能体人工智能的适应性

Adaptation of Agentic AI

摘要

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