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.

翻译：前沿的智能体人工智能系统建立在基础模型之上，这些模型能够适应规划、推理以及与外部工具交互，以执行日益复杂和专门化的任务。随着这些系统在能力和范围上的扩展，适应机制成为提升性能、可靠性和泛化能力的核心手段。本文旨在将快速扩展的研究领域统一为一个系统化框架，该框架涵盖智能体适应与工具适应两个方面。我们进一步将其分解为工具执行信号驱动和智能体输出信号驱动的智能体适应形式，以及智能体无关和智能体监督的工具适应形式。我们证明，该框架有助于厘清智能体人工智能中适应策略的设计空间，明确其权衡取舍，并为系统设计过程中选择或切换策略提供实用指导。随后，我们回顾了各类别中的代表性方法，分析了它们的优势与局限，并指出了关键的开放挑战与未来机遇。总体而言，本文旨在为寻求构建更强大、高效和可靠的智能体人工智能系统的研究人员和实践者提供概念基础与实践路线图。