This could be an interesting new survey paper by Yejin Choi and her team.
From the 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.
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."
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."
Figure 1: Overview of adaptations in agentic AI.
Agent: the foundation models serving as orchestration and reasoning modules;
Tool: callable components other than the agent model that operate independently, e.g., APIs, ML models, subagents, or memory. We categorize these adaptations into two: agent adaptation (A1 & A2): adapting agent models, and tool adaptation (T1 & T2): adapting tools for agents.
Agent: the foundation models serving as orchestration and reasoning modules;
Tool: callable components other than the agent model that operate independently, e.g., APIs, ML models, subagents, or memory. We categorize these adaptations into two: agent adaptation (A1 & A2): adapting agent models, and tool adaptation (T1 & T2): adapting tools for agents.
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