Adaptation of Agentic AI
Pengcheng Jiang, Jiacheng Lin, Zhiyi Shi, Zifeng Wang, Luxi He, Yichen Wu, Ming Zhong, Peiyang Song, Qizheng Zhang, Heng Wang, Xueqiang Xu, Hanwen Xu, Pengrui Han, Dylan Zhang, Jiashuo Sun, Chaoqi Yang, Kun Qian, Tian Wang, Changran Hu, Manling Li, Quanzheng Li, Hao Peng
2025-12-19
Summary
This paper is about how to make AI systems that can think for themselves and use tools to complete tasks more adaptable and effective.
What's the problem?
As AI systems get more complex and are asked to do more things, it's becoming really important for them to be able to adjust and improve. There are lots of different ways researchers are trying to make this happen, but it's a bit chaotic and hard to understand how all the approaches relate to each other. Specifically, it's difficult to categorize how AI adapts – whether it's based on how well it *uses* tools, or how well it *produces* results, and whether a human is guiding the adaptation or not.
What's the solution?
The researchers created a way to organize all the different adaptation techniques into a clear framework. They broke down adaptation into two main categories: how the AI agent itself changes, and how the tools the AI uses change. Then, they further divided these categories based on what triggers the adaptation – whether it’s based on how well the tool works during use, or how good the AI’s final output is. They also considered if a person is involved in guiding the adaptation process. By organizing things this way, they can clearly show the pros and cons of each approach.
Why it matters?
This work is important because it provides a common language and structure for understanding and developing adaptable AI systems. It helps researchers and developers choose the best adaptation strategies for their specific needs, and it points out areas where more research is needed to build even more powerful and reliable AI in the future.
Abstract
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.