Adaptation of Agentic AI
🤖 AI Summary
Existing adaptive mechanism design in agentic AI systems lacks a systematic theoretical framework, hindering principled analysis and comparison of agent- versus tool-level adaptation.
Method: We propose the first unified two-dimensional decomposition framework—distinguishing (i) adaptation locus (agent vs. tool) and (ii) adaptation trigger type (e.g., event-driven, threshold-based) and supervision paradigm (e.g., supervised, reinforcement, self-supervised)—to explicitly characterize adaptation initiation and learning supervision. Through taxonomy construction, comparative evaluation of adaptation strategies, and design-space mapping, we delineate applicability boundaries, performance trade-offs, and intrinsic limitations across adaptation pathways.
Contribution/Results: The framework fills a critical structural gap in agentic AI adaptation theory and provides an actionable, decision-oriented design guide for building more reliable, efficient, and generalizable agent systems.
Application Category
📝 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.
Problem
Research questions and friction points this paper is trying to address.
Unify adaptation research into a systematic framework
Clarify design space and trade-offs of adaptation strategies
Provide guidance for building capable agentic AI systems
Innovation
Methods, ideas, or system contributions that make the work stand out.
Unified framework for agent and tool adaptations
Decomposes adaptations into execution and output signaled forms
Provides practical guidance for strategy selection in design
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