Adaptation of Agentic AI
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.
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Pengcheng Jiang
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2025-12-19T08:58:04.988Z
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Patrick Jiang
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Jiacheng Lin
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2025-12-19T08:58:02.806Z
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Jiacheng Lin
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Zhiyi Shi
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2025-12-19T16:25:52.445Z
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Zifeng Wang
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Luxi He
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Yichen Wu
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Ming Zhong
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Peiyang Song
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2025-12-19T08:58:06.854Z
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Peiyang Song
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Qizheng Zhang
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Qizheng Zhang
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Heng Wang
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2025-12-22T10:59:43.185Z
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Heng Wang
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Xueqiang Xu
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Xueqiang Xu
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Hanwen Xu
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Pengrui Han
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2026-02-11T22:17:30.406Z
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Pengrui Han
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Dylan Zhang
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Jiashuo Sun
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Chaoqi Yang
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Kun Qian
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Tian Wang
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Changran Hu
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Manling Li
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Manling Li
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Quanzheng Li
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Hao Peng
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Sheng Wang
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Jingbo Shang
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Chao Zhang
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Jiaxuan You
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Liyuan Liu
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Pan Lu
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2025-12-25T20:52:33.264Z
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Pan Lu
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Yu Zhang
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Heng Ji
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Yejin Choi
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Dawn Song
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Jimeng Sun
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Jiawei Han
Adaptation of Agentic AI
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.
This paper presents a framework for agent and tool adaptation in agentic AI systems, clarifying design strategies and identifying open challenges for improving AI capabilities.
agentic AI systems foundation models agent adaptations tool adaptations tool-execution-signaled agent-output-signaled agent-agnostic agent-supervised