The field of Artificial Intelligence is undergoing a transition from Generative AI -- probabilistic generation of text and images -- to Agentic AI, in which autonomous systems execute actions within external environments on behalf of users. This transition exposes a fundamental architectural mismatch: Large Language Models (LLMs) produce stochastic, unstructured outputs, whereas the backend infrastructure they must control -- databases, APIs, cloud services -- requires deterministic, schema-conformant inputs. The present paper describes the Auton Agentic AI Framework, a principled architecture for standardizing the creation, execution, and governance of autonomous agent systems. The framework is organized around a strict separation between the Cognitive Blueprint, a declarative, language-agnostic specification of agent identity and capabilities, and the Runtime Engine, the platform-specific execution substrate that instantiates and runs the agent. This separation enables cross-language portability, formal auditability, and modular tool integration via the Model Context Protocol (MCP). The paper formalizes the agent execution model as an augmented Partially Observable Markov Decision Process (POMDP) with a latent reasoning space, introduces a hierarchical memory consolidation architecture inspired by biological episodic memory systems, defines a constraint manifold formalism for safety enforcement via policy projection rather than post-hoc filtering, presents a three-level self-evolution framework spanning in-context adaptation through reinforcement learning, and describes runtime optimizations -- including parallel graph execution, speculative inference, and dynamic context pruning -- that reduce end-to-end latency for multi-step agent workflows.
EvoTrainer: Co-Evolving LLM Policies and Training Harnesses for Autonomous Agentic Reinforcement Learning
Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences（深圳先进技术研究院，中国科学院）；Tongyi Lab , Alibaba Group（通义实验室，阿里巴巴集团）；Alibaba Group（阿里巴巴集团）；SUAT（深圳大学）
Agentic Router: An Execution-Grounded Continual Learning Approach With Memory
Zhejiang University（浙江大学）；Zhejiang Lab（之江实验室）；State Grid Hebei Electric Power Co., Ltd.（国网河北省电力有限公司）；Macau University of Science and Technology（澳门科技大学）
HyEvo: Self-Evolving Hybrid Agentic Workflows for Efficient Reasoning
East China Normal University（华东师范大学）；Beihang University（北京航空航天大学）；Shanghai University of International Business and Economics（上海对外经贸大学）；Fudan University（复旦大学）
AINTMA: Agentic AI Architecture for Autonomous Test Management with Generative Intelligence, Secure Cloud Communication and Adaptive Quality Analytics
International Business Machines (IBM)（国际商业机器公司（IBM））；Global Atlantic Financial（全球大西洋金融公司）；Docusign（DocuSign公司）；Salesforce Inc（Salesforce公司）；The Home Depot（家得宝公司）
Semantic Chain-of-Trust: Autonomous Trust Orchestration for Collaborator Selection via Hypergraph-Aided Agentic AI
Department of Electrical and Computer Engineering, Western University（电气与计算机工程系，西部大学）；College of Computing and Data Science, Nanyang Technological University（计算与数据科学学院，南洋理工大学）
City Editing: Hierarchical Agentic Execution for Dependency-Aware Urban Geospatial Modification
University of Kansas, Lawrence, KS, USA（堪萨斯大学）；Seoul National University, Seoul, South Korea（首尔国立大学）；University of Florida, Gainesville, FL, USA（佛罗里达大学）；NEC Laboratories America, Princeton, NJ, USA（NEC美国实验室）；Clemson University, Clemson, SC, USA（克莱姆森大学）；Arizona State University, Tempe, AZ, USA（亚利桑那州立大学）
From Agentification to Self-Evolving Agentic AI for Wireless Networks: Concepts, Approaches, and Future Research Directions
College of Computing and Data Science, Nanyang Technological University（计算与数据科学学院，南洋理工大学）；College of Computer Science and Technology, Jilin University（计算机科学与技术学院，吉林大学）；Department of Electrical and Computer Engineering, Western University（电气与计算机工程系，西方大学）；Department of Electrical and Computer Engineering, Auburn University（电气与计算机工程系，阿伯茨罕大学）；School of Electrical and Computer Engineering, University of Sydney（电气与计算机工程学院，悉尼大学）
An Agentic Framework for Autonomous Metamaterial Modeling and Inverse Design
Department of Electrical and Computer Engineering, Duke University, Durham, USA（电气与计算机工程系，杜克大学，达勒姆，美国）；Department of Electrical Engineering and Computer Science, University of Missouri, Columbia, USA（电气工程与计算机科学系，密苏里大学，哥伦比亚，美国）
SW-$A^2$-Bench: Benchmarking Autonomous Software Agent Generation for Agentic Web
Shanghai Jiao Tong University（上海交通大学）；The University of Tokyo（东京大学）；Huazhong University of Science and Technology（华中科技大学）；Shanghai Innovation Institute（上海创新研究院）；Nankai University（南开大学）；Singapore University of Technology and Design（新加坡科技设计大学）；Queen’s University（女王大学）；Fudan University（复旦大学）；Zhejiang University（浙江大学）
El Agente Potente: High-Throughput Agentic Atomistic Simulations
University of Toronto（多伦多大学）；Vector Institute for Artificial Intelligence（向量人工智能研究所）；Acceleration Consortium（加速联盟）；Canadian Institute for Advanced Research (CIFAR)（加拿大高等研究院（CIFAR））；NVIDIA（英伟达）
