Beyond Pipelines: A Survey of the Paradigm Shift toward Model-Native
  Agentic AI
The rapid evolution of agentic AI marks a new phase in artificial
intelligence, where Large Language Models (LLMs) no longer merely respond but
act, reason, and adapt. This survey traces the paradigm shift in building
agentic AI: from Pipeline-based systems, where planning, tool use, and memory
are orchestrated by external logic, to the emerging Model-native paradigm,
where these capabilities are internalized within the model's parameters. We
first position Reinforcement Learning (RL) as the algorithmic engine enabling
this paradigm shift. By reframing learning from imitating static data to
outcome-driven exploration, RL underpins a unified solution of LLM + RL + Task
across language, vision and embodied domains. Building on this, the survey
systematically reviews how each capability -- Planning, Tool use, and Memory --
has evolved from externally scripted modules to end-to-end learned behaviors.
Furthermore, it examines how this paradigm shift has reshaped major agent
applications, specifically the Deep Research agent emphasizing long-horizon
reasoning and the GUI agent emphasizing embodied interaction. We conclude by
discussing the continued internalization of agentic capabilities like
Multi-agent collaboration and Reflection, alongside the evolving roles of the
system and model layers in future agentic AI. Together, these developments
outline a coherent trajectory toward model-native agentic AI as an integrated
learning and interaction framework, marking the transition from constructing
systems that apply intelligence to developing models that grow intelligence
through experience.
68f6ebbc24c4489363111823
Jitao Sang
68f6ebbc24c4489363111824
Jinlin Xiao
68f6ebbc24c4489363111825
Jiarun Han
68f6ebbc24c4489363111826
Jilin Chen
68f6ebbc24c4489363111827
Xiaoyi Chen
68f6ebbc24c4489363111828
Shuyu Wei
68f6ebbc24c4489363111829
Yongjie Sun
68f6ebbc24c448936311182a
Yuhang Wang
Beyond Pipelines: A Survey of the Paradigm Shift toward Model-Native
  Agentic AI
The rapid evolution of agentic AI marks a new phase in artificial
intelligence, where Large Language Models (LLMs) no longer merely respond but
act, reason, and adapt. This survey traces the paradigm shift in building
agentic AI: from Pipeline-based systems, where planning, tool use, and memory
are orchestrated by external logic, to the emerging Model-native paradigm,
where these capabilities are internalized within the model's parameters. We
first position Reinforcement Learning (RL) as the algorithmic engine enabling
this paradigm shift. By reframing learning from imitating static data to
outcome-driven exploration, RL underpins a unified solution of LLM + RL + Task
across language, vision and embodied domains. Building on this, the survey
systematically reviews how each capability -- Planning, Tool use, and Memory --
has evolved from externally scripted modules to end-to-end learned behaviors.
Furthermore, it examines how this paradigm shift has reshaped major agent
applications, specifically the Deep Research agent emphasizing long-horizon
reasoning and the GUI agent emphasizing embodied interaction. We conclude by
discussing the continued internalization of agentic capabilities like
Multi-agent collaboration and Reflection, alongside the evolving roles of the
system and model layers in future agentic AI. Together, these developments
outline a coherent trajectory toward model-native agentic AI as an integrated
learning and interaction framework, marking the transition from constructing
systems that apply intelligence to developing models that grow intelligence
through experience.
The survey outlines the shift from pipeline-based to model-native agentic AI, emphasizing the role of reinforcement learning in integrating planning, tool use, and memory within large language models across various domains.
Reinforcement Learning Model-native paradigm Large Language Models Planning Tool use Memory Deep Research agent GUI agent Multi-agent collaboration Reflection