Adaptation of Agentic AI (2512.16301v1)

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.

• The paper introduces a formal taxonomy that categorizes adaptation mechanisms of agentic AI into four distinct paradigms based on supervision origin and optimization locus.
• It details the evolution of both agent and tool adaptation via methods such as SFT, RL, and self-refinement, highlighting dramatic data efficiency gains in T2 methods.
• The analysis emphasizes practical implications for modular design, safe continual learning, and robust tool integration across diverse application domains.

Adaptation Mechanisms in Agentic AI: A Formal Synthesis

Introduction

Agentic AI systems—autonomous entities capable of planning, reasoning, and multi-step tool use—have advanced substantially through foundation models such as LLMs. However, scaling these systems for complex, open-ended tasks has highlighted persistent weaknesses in robustness, domain generalization, and effective tool integration. The paper "Adaptation of Agentic AI" (2512.16301) presents a unified, formal taxonomy organizing the adaptation mechanisms available for enhancing agentic AI. This essay synthesizes its technical contributions, methodological structuring, empirical and theoretical insights, and implications for the future of adaptive, modular, and reliable agentic AI.

Formal Framework for Adaptation in Agentic AI

The paper establishes a $2\times2$ taxonomy for adaptation according to two axes: the locus of optimization (agent vs. tool) and the origin of the supervision signal (tool execution vs. agent output). This yields four canonical paradigms (see also Fig. 1 and Fig. 3):

1. A1: Tool Execution-Signaled Agent Adaptation: The agent is updated using verifiable outcome signals arising from the actual execution of external tools (e.g., code pass rate, retrieval score).
2. A2: Agent Output-Signaled Agent Adaptation: The agent is adapted using evaluations of its generated outputs, possibly integrating tool results, with feedback reflecting outcome quality (e.g., answer correctness, preference alignment).
3. T1: Agent-Agnostic Tool Adaptation: Tools (retrievers, planners, subagents) are optimized independently of the agent, resulting in plug-and-play modules that can be orchestrated by a fixed agent.
4. T2: Agent-Supervised Tool Adaptation: The agent is frozen, and tools are adapted through supervision derived from agent outputs (e.g., reward-driven retriever tuning, planner alignment).

   Figure 1: Overview of adaptation mechanisms—A1 and A2 for agent adaptation, T1 and T2 for tool adaptation—framed around foundation models and callable external components.

   

   Figure 3: The four adaptation paradigms visualized, emphasizing which system components are directly optimized and where learning signals originate.

This structure makes explicit the architectural trade-offs between parametric flexibility, system modularity, data efficiency, and evolution ergonomics—a critical contribution for both theoretical unification and practical system design in agentic AI.

Agent Adaptation Paradigms: Technical Evolution and Empirical Findings

A1: Tool Execution-Signaled Agent Adaptation

The A1 paradigm optimizes the agent with signals provided directly by tool execution (e.g., code tests, SQL execution, retrieval rewards). The evolution of methods is structured into three technical branches, shown in chronological development in Fig. 4:

• Self-supervised and SFT/DPO methods: Toolformer [schick2023toolformer] introduces self-supervised learning from tool-augmented language modeling, retaining tool calls that reduce model perplexity. SFT and DPO-style approaches further align agents to correct tool-use trajectories, as with TRICE or ToolAlpaca.
• Format- and structure-based alignment: Gorila [patil2024gorilla] introduces AST-based correctness signals for API tool use; ToolFlow uses graph-based planning and preference data mined from failed interactions.
• Reinforcement learning with verifiable rewards (RLVR): This major advance, typified by DeepRetrieval [jiang2025deepretrieval], RLEF [gehring2025rlefgroundingcodellms], and others, formalizes agent-tool interaction as MDPs, with RL optimizing agent policies using tool-outcome-derived rewards. Empirical results in retrieval and code domains demonstrate strong gains—e.g., DeepRetrieval attains a threefold recall improvement (65.1% vs. 24.7%) for literature search.

  Figure 2: Timeline tracking the evolution of A1 methods from SFT through RLVR regimes for agent adaptation based on tool execution feedback.

A1 methods achieve strong mechanistic competence where the supervision signal is deterministic, dense, and verifiable.

A2: Agent Output-Signaled Agent Adaptation

A2 adapts the agent using signals from agent outputs—final answers, completed programs, or iteratively-refined reasoning. This includes both tool-free and tool-augmented settings, with either SFT/DPO or RLVR as the optimizer. The technical progression (Fig. 5) spans:

• Reasoning-centric RL: DeepSeek-R1 [guo2025deepseek] and Kimi-1.5 demonstrate large-scale reasoning enhancement via verifiable output rewards in math and code, establishing the "R1 paradigm."
• Self-refinement and meta-feedback: Methods such as Self-Refine and TextGrad [yuksekgonul2025optimizing] propagate LLM feedback as textual gradients—a parameter-agnostic form of output-based adaptation supporting black-box LLMs and test-time learning.
• Multi-tool orchestration: RL approaches like ReTool train agents to optimize tool use strategies holistically via outcome-based reward, with reflection and multi-stage rollouts.

  Figure 4: Chronology of A2 methods showcasing diverse paradigms for agent adaptation with supervision from agent outputs, integrating self-refinement, RL, and meta-feedback.

A2 offers end-to-end policy optimization for complex, open-ended domains, but incurs high training cost and potential overfitting when managing monolithic models across multiple tasks.

Tool Adaptation Paradigms: Modular Ecosystems and Symbiotic Inversion

T1: Agent-Agnostic Tool Adaptation

T1 comprises models and subagents trained independently from the orchestrating agent. Foundational architectures include:

• Plug-and-play vision models: CLIP [radford2021learning], SAM [kirillov2023segment], and multimodal systems provide robust perceptual tools.
• Scientific simulation engines: Neural operators and domain-specialized predictors enable function approximators and modeling APIs.
• Code and search agents graduated as tools: Trained search agents (e.g., DeepRetrieval) and code agents (e.g., Code-R1) are frozen and reused as T1 subcomponents, supporting modular assembly of pipelines.

The central property of T1 is system-level flexibility, allowing arbitrary composition while constraining tool performance to what the agent can leverage.

T2: Agent-Supervised Tool Adaptation

T2 operationalizes the "symbiotic inversion"—lightweight tools (retrievers, planners, memory modules, advisors) adapted under frozen agent supervision. Technical advances include:

• Proxy-signaled retrievers: REPLUG [shi2024replug] and BLADE implement perplexity-based and preference-based alignment for retrievers optimized solely from agent feedback, bypassing direct access to agent parameters.
• Preference distillation and multi-stage pipelines: LLM-R and BGM integrate multi-stage learning signals—likelihood preferences, answer gains, outcome rewards—to build adaptive, agent-aligned retrieval and planning components.
• Agentic subagents and memory: s3 [jiang2025s3] demonstrates that a 7B parameter search subagent, trained with only 2.4k examples and guided by frozen agent evaluations, can match or outperform monolithic end-to-end agents trained with 70x more data (A2 paradigm), and generalizes better to new domains. Memory modules such as Memento [zhou2025memento] train retrieval and storage policies entirely from binary agent outcome rewards, enhancing generalization for long-horizon reasoning.

  Figure 5: Developmental trajectory of T2 methods, emphasizing the rise of agent-supervised tool adaptation, agentic subagents, and memory modules in symbiotic ecosystems.

T2 achieves strong data efficiency, modular extensibility, and minimizes catastrophic forgetting by localizing adaptation to peripheral subagents.

Comparative Analysis and Architectural Implications

The framework yields several salient results:

• Data efficiency: T2 methods (e.g., s3) reach parity with monolithic A2 agents using $70\times$ less labeled data, demonstrating an order-of-magnitude gap in data requirement due to focused procedural learning in small subagents.
• Modularity: T1 and T2 paradigms enable modular system evolution—adding, replacing, or updating tools for new capabilities without destabilizing the agent core—crucial for production and federated pipelines.
• Risk of overfitting and forgetting: A1/A2 paradigms, though powerful for capability emergence, are susceptible to catastrophic forgetting and monolithic retraining costs; T2 enables safer incremental upgrades.

The taxonomy also explicitly highlights strong claims regarding architectural trade-offs, such as the superiority of T2 in data efficiency and system evolvability for procedural skills, and the nontrivial risk in monolithic A2 approaches where reasoning, knowledge, and tool use are entangled during adaptation.

Applications Across Domains

The implications of this formalism extend across multiple verticals:

• Deep research agents: Systems such as DeepResearch integrate both A2 and T2 strategies to conduct end-to-end scientific inquiry, requiring both robust planning and tool augmentation [xu2025comprehensive].
• Software development: Hybrid A1/T2 pipelines enable agents to autonomously edit, debug, and test code by leveraging RL-trained subagents and adaptive tool-chains (see SWE-Grep, SWE-Agent).
• GUI and computer-use agents: Modular T2-style adaptation of perception, memory, and control components supports robust generalization and test-time learning in interactive, visual environments.
• Biomedicine and scientific computing: The integration of agent-adapted reasoning and domain-specialized, T2-optimized tools (retrieval, simulation, analytics) accelerates end-to-end pipelines for drug discovery and clinical research.

  Figure 6: Representative applications of adaptation strategies in agentic AI, spanning scientific research, software engineering, computer use, and biomedical domains.

Open Challenges and Future Trajectories

The authors identify multiple theoretically and practically salient research frontiers:

• Co-adaptation and federated optimization: Moving beyond the dichotomy of frozen versus adaptive components to bi-level or multi-agent optimization, with reciprocal adaptation between agents and tools, remains open. Game-theoretic stability, reward design, and credit assignment are principal obstacles.
• Continual adaptation and lifelong learning: Real-world deployment entails non-stationary task distributions and evolving operational environments. Integration of continual learning methods and memory modules aligned with the T1/T2 perspective is critical.
• Safe and efficient adaptation: RL-driven methods must mitigate unsafe exploration and reward hacking. The separation of skill from knowledge in T2-centric pipelines offers promising avenues for more interpretable, controllable, and verifiable adaptation. Parameter-efficient and on-device learning methods (LoRA, quantization) further lower barriers to scalable, private, and low-cost agent evolution.

Conclusion

This paper provides a formal, systematic taxonomy for adaptation in agentic AI, rigorously structuring the design landscape along axes of agent-versus-tool and execution-versus-output supervision signals. Its critical insights include the demonstration of dramatically higher data efficiency and system resiliency in T2-style tool adaptation, the architectural advantages of modular and federated agent-tool pipelines, and the explicit articulation of challenges for safe, continual, and co-adaptive learning. The framework and analysis set a reference point for the principled design, evaluation, and evolution of adaptive agentic AI systems in both research and industrial settings (2512.16301).