Overview of Agentic AI Paradigm Shift

The article presents a comprehensive survey on agentic AI, tracing a fundamental paradigm shift from traditional Pipeline-based systems to an emerging Model-native paradigm. This transition signifies Large Language Models (LLMs) internalizing capabilities like planning, tool use, and memory, moving beyond external orchestration. Reinforcement Learning (RL) is positioned as the pivotal algorithmic engine driving this transformation, enabling LLMs to learn through outcome-driven exploration rather than static data imitation. The survey systematically reviews how core agentic capabilities have evolved and examines their impact on key applications such as Deep Research and GUI agents, ultimately outlining a trajectory towards integrated learning and interaction frameworks.

Critical Evaluation of Agentic AI Evolution

Strengths

This survey’s primary strength lies in its comprehensive and structured analysis of a rapidly evolving field. It clearly articulates the paradigm shift from externally orchestrated to integrated, model-native AI systems, offering a valuable framework for understanding current and future developments. The detailed breakdown of how Reinforcement Learning underpins the internalization of crucial capabilities—planning, tool use, and memory—is particularly insightful. By examining specific applications like Deep Research and GUI agents, the article effectively illustrates practical implications and offers a compelling vision for future directions in AI development.

Weaknesses

While highly informative, as a survey, the article inherently prioritizes breadth over depth. It outlines numerous advancements but might not delve into intricate practical challenges or empirical trade-offs associated with implementing model-native solutions, such as computational costs or credit assignment complexities. The field’s rapid evolution means some specific methods discussed could quickly become outdated. Additionally, advocating for RL as a “unified solution” could benefit from a more critical discussion of current limitations and hurdles in achieving this methodological singularity, particularly regarding the scalability and empirical validation of advanced RL algorithms in real-world scenarios.

Implications

The insights presented carry significant implications for the future of AI development. The shift towards model-native agentic AI suggests a future where systems are not merely applying pre-programmed intelligence but are actively “growing intelligence through experience.” This trajectory promises more autonomous, adaptive, and robust autonomous agents capable of complex reasoning and interaction. It underscores Reinforcement Learning’s critical role in fostering this evolution, pushing research towards more integrated learning and interaction frameworks across various domains, from scientific discovery to human-computer interaction.

Conclusion: Impact and Future of Model-Native Agentic AI

This survey offers a timely and essential contribution to the scientific discourse on agentic AI, providing a clear roadmap for understanding its transformative potential. By meticulously detailing the transition from pipeline-based to model-native paradigms, driven by Reinforcement Learning, it not only synthesizes current advancements but also illuminates the path for future research. The article’s value lies in its ability to consolidate a vast and complex topic into a coherent narrative, making it an indispensable resource for researchers and practitioners navigating the evolving landscape of intelligent agents.

Navigating the Paradigm Shift: A Comprehensive Analysis of Agentic AI’s Evolution

The landscape of artificial intelligence is undergoing a profound transformation, moving beyond reactive systems to embrace a new era of agentic AI. This pivotal shift, meticulously surveyed in the article, highlights how Large Language Models (LLMs) are evolving from mere responders into autonomous entities capable of acting, reasoning, and adapting. The core of this evolution lies in the transition from externally orchestrated, Pipeline-based systems to an emerging Model-native paradigm, where critical capabilities like planning, tool use, and memory are internalized within the model’s parameters. The article positions Reinforcement Learning (RL) as the fundamental algorithmic engine driving this change, enabling LLMs to learn through outcome-driven exploration rather than static data imitation. By systematically reviewing the evolution of these core capabilities and their impact on applications such as Deep Research and GUI agents, the survey outlines a coherent trajectory towards an integrated learning and interaction framework, where models actively grow intelligence through experience rather than simply applying it.

Critical Evaluation: Unpacking the Trajectory of Model-Native Agentic AI

Strengths: A Visionary Framework for Agentic AI

One of the most significant strengths of this analysis is its comprehensive and systematic articulation of the paradigm shift in agentic AI. The clear distinction between Pipeline-based and Model-native approaches provides a robust conceptual framework for understanding the current state and future direction of the field. This clarity is crucial for both seasoned researchers and newcomers, offering a structured lens through which to view the rapid advancements in LLM capabilities. The survey meticulously traces how core functionalities, traditionally managed by external logic, are increasingly being internalized, marking a fundamental change in how intelligent systems are designed and developed. This conceptual clarity underpins the entire discussion, making complex evolutionary pathways accessible and understandable.

The article’s emphasis on Reinforcement Learning (RL) as the primary algorithmic engine for this transformation is another major strength. By reframing learning from imitation to outcome-driven exploration, RL is presented as the unifying solution across diverse domains, including language, vision, and embodied tasks. This perspective is vital, as it highlights how RL enables LLMs to tackle complex, multi-step challenges that are difficult to address with traditional supervised fine-tuning methods due to procedural data scarcity. The discussion of advanced RL algorithms, such as Group Relative Policy Optimization (GRPO) and Decoupled Clip and Dynamic sAmpling Policy Optimization (DAPO), further underscores the technical depth and forward-thinking nature of the survey, showcasing how these methods are crucial for addressing long-horizon challenges in agentic AI.

Furthermore, the survey excels in its structured review of how each core capability—Planning, Tool Use, and Memory—has evolved. For planning, the analysis moves from hybrid LLM+PDDL systems and prompt-based methods like Chain-of-Thought (CoT) and Tree-of-Thought (ToT) to model-native internalization, often driven by outcome rewards and structural supervision. Similarly, for tool use, it details the shift from external, system-based or prompt-based methods to internalized, end-to-end learned policies that offer improved generalization and dynamic adaptation. The discussion on memory is equally thorough, tracing its evolution from pipeline-based methods like Retrieval-Augmented Generation (RAG) and long-context processing techniques to model-native solutions involving position encoding extrapolation and attention optimization. This systematic breakdown provides a clear, evolutionary narrative for each critical component of agentic intelligence.

The inclusion of specific application examples, such as Deep Research agents and GUI agents, significantly enhances the article’s practical relevance and accessibility. These case studies illustrate how the paradigm shift from pipeline-based to model-native approaches impacts real-world AI systems. For Deep Research agents, the survey explores their evolution in long-horizon reasoning and information acquisition, detailing various architectures and knowledge acquisition methods. For GUI agents, it traces their development from early record-and-replay systems to advanced prompt-based and model-native paradigms, emphasizing end-to-end training and the internalization of perception and planning. These concrete examples ground the theoretical discussions in tangible applications, demonstrating the practical implications of the model-native trajectory.

Finally, the article’s forward-looking perspective, discussing the continued internalization of capabilities like Multi-agent collaboration and Reflection, is a notable strength. It anticipates future trends, such as distilling Multi-agent Reinforcement Learning (MARL) behaviors into single models and shifting reflection from external processes to internal self-correction mechanisms. This visionary outlook not only highlights ongoing research frontiers but also frames the broader trajectory of agentic AI towards systems that can autonomously grow intelligence through experience. The discussion on evolving roles of system and model layers, alongside challenges in design, training, and interpretability, provides a balanced view of the exciting yet complex path ahead for model-native agentic AI.

Weaknesses: Navigating the Hurdles of Internalization

While the survey provides a compelling vision, it also implicitly highlights several inherent weaknesses and challenges within the current state of agentic AI development. One significant concern revolves around the limitations of existing methods, particularly the Out-of-Distribution (OOD) fragility associated with Chain-of-Thought (CoT) prompting. As noted, externalizing reasoning through CoT can lead to brittle systems that struggle when encountering scenarios outside their training distribution. This fragility underscores the necessity of the model-native paradigm, but also points to the difficulty of achieving robust, generalized reasoning even with advanced prompting techniques, suggesting that true internalization is a complex and ongoing challenge.

Another area of weakness lies in the practical implementation of reward mechanisms for reasoning models. The discussion on “Process Reward” models (PRMs) reveals challenges such as subjectivity, data scarcity, and the potential for reward hacking. While outcome rewards offer objectivity for verifiable tasks, the nuanced nature of complex reasoning often requires more intricate feedback. The difficulties in designing and acquiring high-quality process rewards can impede the effective training of model-native agents, making it harder for them to learn sophisticated internal reasoning processes. This highlights a fundamental bottleneck in moving beyond simple task completion to truly intelligent, adaptable behavior.

The survey also touches upon significant challenges related to training instability and credit assignment, particularly in multi-turn agentic AI and end-to-end training scenarios. Online Reinforcement Learning (RL) for tool use, for instance, can suffer from instability, requiring solutions like asynchronous parallelism and hybrid data approaches. The problem of cross-step credit assignment, especially in long trajectories, remains a formidable hurdle. While trajectory-level rewards are simple and scalable, they can be sparse, making it difficult to attribute success or failure to specific actions. Step-level credit assignment, though finer-grained, introduces its own complexities, impacting the efficiency and effectiveness of learning in dynamic environments. These issues underscore the engineering complexities involved in building robust model-native agents.

Furthermore, the reliance on data-driven approaches, even with RL, brings forth the persistent problem of data scarcity. For supervised learning methods aimed at acquiring planning capabilities, data synthesis via multi-path reasoning-trajectory sampling and Monte Carlo Tree Search (MCTS) is necessary to generate high-quality CoT data. Similarly, for GUI agents, the evolution from imitation learning to online RL is often constrained by the availability of diverse and representative data. This scarcity necessitates sophisticated data generation and distillation techniques, adding layers of complexity to the training pipeline and potentially limiting the generalizability of learned behaviors. Overcoming this data bottleneck is critical for the widespread adoption of model-native agents.

Finally, the computational and inference costs associated with advanced agentic AI models, particularly those employing complex planning methods or extensive context management, present a practical weakness. Prompt-based methods, while flexible, can incur significant inference costs, and the computational demands of training large, end-to-end model-native agents are substantial. This economic barrier can limit accessibility and scalability, especially for smaller research groups or applications with tight resource constraints. Addressing these efficiency concerns will be crucial for translating theoretical advancements into widely deployable and practical agentic AI systems.

Caveats and Limitations: Inherent Challenges in the Pursuit of Model-Native Intelligence

The journey towards model-native agentic AI, as illuminated by the survey, is fraught with inherent caveats and limitations that extend beyond specific methodological weaknesses. One primary caveat is the sheer complexity of internalizing capabilities that were previously handled by explicit, external logic. While the model-native paradigm promises greater autonomy and adaptability, it also introduces immense challenges in terms of model architecture, training methodologies, and ensuring robust, predictable behavior. The transition from a modular, pipeline-based system to an integrated, end-to-end learned system means that failures can be harder to diagnose and control, requiring sophisticated techniques for self-correction and interpretability.

Another significant limitation lies in the trade-offs between different training paradigms, particularly between offline and online agent training. Offline methods offer stability and efficiency by leveraging pre-collected data, but they can suffer from data freshness issues and may not generalize well to dynamic, unseen environments. Online training, while offering adaptability and the ability to learn from real-time interactions, often faces challenges like instability, sample inefficiency, and the need for robust exploration strategies. The choice between these paradigms, or the development of effective hybrid approaches, remains a critical design consideration, with no single solution universally superior across all agentic tasks and environments.

The evolving roles of the system and model layers represent a continuous challenge and a key area of ongoing research. As capabilities become increasingly internalized, the boundary between what the underlying model handles and what external system logic still orchestrates becomes blurred. This dynamic interplay requires careful consideration to optimize performance, ensure safety, and maintain control. The survey hints at this by discussing the “evolving roles of the system and model layers in future agentic AI,” suggesting that achieving a perfect balance or complete internalization is a long-term goal with many intermediate stages and design choices.

Furthermore, the development of advanced benchmarks and scalable evaluation methods is a critical caveat for the progress of model-native agents. As agents become more complex and their behaviors more emergent, traditional evaluation metrics may fall short in capturing their true capabilities, robustness, and ethical implications. The need for personalized governance and more sophisticated evaluation frameworks, especially for memory-enhanced agents and those operating in open-ended environments, is paramount. Without robust evaluation, it becomes difficult to accurately measure progress, compare different approaches, and ensure the responsible development of increasingly autonomous AI systems.

Finally, while the concept of a “unified AI framework” analogous to Newton’s physics is presented as a positive implication, the practical realization of such a unified framework remains a significant caveat. The diversity of tasks, environments, and inherent complexities across language, vision, and embodied domains suggests that a truly singular solution might be elusive or require unprecedented computational resources and theoretical breakthroughs. The survey outlines a coherent trajectory, but the path to a fully integrated learning and interaction framework, where models genuinely “grow intelligence through experience,” is still in its nascent stages, requiring continuous innovation across multiple research fronts.

Implications and Future Directions: Towards Self-Evolving Intelligence

The comprehensive analysis presented in the article carries profound implications for the future of artificial intelligence, signaling a fundamental shift in how we conceive and develop intelligent systems. The most significant implication is the redefinition of Reinforcement Learning as a general optimizer, transforming it into the algorithmic engine that drives the conversion of compute into intelligence through data synthesis. This shift suggests a future where AI systems are not merely trained on static datasets but actively learn and improve through continuous interaction and exploration, generating their own extrapolative and interventional data in a self-improving feedback loop. This paradigm promises to unlock unprecedented levels of autonomy and capability in AI agents.

The trajectory towards continued internalization of agentic capabilities, such as multi-agent collaboration and reflection, is another critical implication. As the survey details, future agents will move beyond externally scripted collaborative strategies to internalize these behaviors, potentially distilling complex multi-agent reinforcement learning (MARL) dynamics into single, more efficient models. Similarly, reflection will evolve from pipeline-based external checks to model-native self-correction, enhancing autonomy and robustness. This ongoing internalization signifies a move towards truly self-contained and self-improving AI, capable of sophisticated reasoning and adaptation without constant human oversight or external orchestration.

The evolution of base models from Large Language Models (LLMs) to Large Retrieval Models (LRMs) and Large Multimodal Models (LMMs) also has significant implications. This diversification indicates a future where agents are not limited to textual understanding but can seamlessly integrate and process information from various modalities, including vision, audio, and structured data. This multimodal capability will enable agents to interact with the world in a richer, more human-like manner, expanding their applicability to a vast array of complex tasks that require understanding and reasoning across different data types. The development of hybrid architectures and robust training methods will be crucial for harnessing the full potential of these advanced base models.

Furthermore, the article points towards the emergence of integrated learning and interaction frameworks, where AI models are designed to “grow intelligence through experience.” This vision moves beyond the traditional view of AI as a tool that applies pre-programmed intelligence, towards systems that continuously learn, adapt, and evolve their capabilities. This implies a future where AI agents are not static but dynamic, capable of acquiring new skills, refining existing ones, and even creating new tools or strategies autonomously. This trajectory promises to deliver more versatile, resilient, and ultimately more intelligent AI systems that can operate effectively in complex, unpredictable environments.

Finally, the discussion on future trends, including advanced benchmarks, capability internalization, and personalized governance for memory-enhanced agents, highlights the broader societal and ethical implications. As agents become more autonomous and capable of long-term memory and personalized interaction, the need for robust governance frameworks becomes paramount. This includes developing methods to ensure fairness, transparency, and accountability in agent behavior, as well as designing systems that can be safely controlled and aligned with human values. The survey thus not only charts a technical path but also implicitly calls for a thoughtful consideration of the responsible development and deployment of these increasingly powerful AI entities.

Conclusion: Charting the Course for Model-Native Agentic AI

The article provides an exceptionally insightful and comprehensive analysis of the paradigm shift currently reshaping the field of artificial intelligence, moving decisively towards model-native agentic AI. By meticulously detailing the evolution from externally orchestrated, Pipeline-based systems to integrated, self-contained models, the survey offers a crucial roadmap for understanding the future of intelligent systems. Its emphasis on Reinforcement Learning as the foundational algorithmic engine, coupled with a systematic review of how core capabilities like planning, tool use, and memory are being internalized, underscores the profound transformation underway. The practical examples of Deep Research and GUI agents further illustrate the tangible impact of this shift, grounding theoretical concepts in real-world applications.

While acknowledging the significant challenges—ranging from data scarcity and training instability to the complexities of credit assignment and reward design—the article maintains a forward-looking perspective. It effectively highlights the ongoing research frontiers, such as multi-agent collaboration and reflection, and anticipates the emergence of truly self-improving AI that grows intelligence through continuous experience. This survey is invaluable for anyone seeking to grasp the current state and future trajectory of agentic AI, offering a coherent narrative that synthesizes diverse research threads into a unified vision. Its clarity, depth, and strategic foresight make it an essential resource for researchers, developers, and enthusiasts navigating this exciting new phase of artificial intelligence development, ultimately contributing significantly to our understanding of how AI models are transitioning from applying intelligence to actively cultivating it.