Large Language Models (LLMs) are evolving into autonomous agents, yet current "frameless" development--relying on ambiguous natural language without engineering blueprints--leads to critical risks such as scope creep and open-loop failures. To ensure industrial-grade reliability, this study proposes Agentic Problem Frames (APF), a systematic engineering framework that shifts focus from internal model intelligence to the structured interaction between the agent and its environment.
The APF establishes a dynamic specification paradigm where intent is concretized at runtime through domain knowledge injection. At its core, the Act-Verify-Refine (AVR) loop functions as a closed-loop control system that transforms execution results into verified knowledge assets, driving system behavior toward asymptotic convergence to mission requirements (R). To operationalize this, this study introduces the Agentic Job Description (AJD), a formal specification tool that defines jurisdictional boundaries, operational contexts, and epistemic evaluation criteria.
The efficacy of this framework is validated through two contrasting case studies: a delegated proxy model for business travel and an autonomous supervisor model for industrial equipment management. By applying AJD-based specification and APF modeling to these scenarios, the analysis demonstrates how operational scenarios are systematically controlled within defined boundaries. These cases provide a conceptual proof that agent reliability stems not from a model's internal reasoning alone, but from the rigorous engineering structures that anchor stochastic AI within deterministic business processes, thereby enabling the development of verifiable and dependable domain agents.
Engineering Trustworthy Agentic AI for Critical Systems
Texas A&M University（德克萨斯A&M大学）；Innovations in Systems Trust and Resilience (iSTAR) Laboratory（系统信任与弹性创新（iSTAR）实验室）；Texas A&M Institute of Data Science - Security, Privacy and Resilience for Trusted AI (SPARTA) Thematic Lab.（德克萨斯A&M大学数据科学研究所 - 可信人工智能的安全、隐私与弹性（SPARTA）主题实验室）；Texas A&M Global Cyber Research Institute (GCRI)（德克萨斯A&M大学全球网络研究所）
DUCTILE: Agentic LLM Orchestration of Engineering Analysis in Product Development Practice
Product Development Division, Department of Mechanical Engineering, Chalmers University of Technology, Gothenburg, Sweden（产品开发部门，机械工程系，楚姆勒斯技术大学，哥德堡，瑞典）；GKN Aerospace Engine Systems, Trollhättan, Sweden（GKN航空发动机系统，特罗尔哈塔纳，瑞典）
From Code-Centric to Intent-Centric Software Engineering: A Reflexive Thematic Analysis of Generative AI, Agentic Systems, and Engineering Accountability
REVEAL: Reverse Engineering Agentic Latents from Frontier-Model Traces
Zhejiang University（浙江大学）；South China University of Technology（华南理工大学）；Alibaba Group（阿里巴巴集团）；Fudan University（复旦大学）；City University of Hong Kong（香港城市大学）
A Practical Guide for Designing, Developing, and Deploying Production-Grade Agentic AI Workflows
Old Dominion University（旧 Dominion 大学）；Florida International University（佛罗里达国际大学）；Nanyang Technological University（南洋理工大学）；University of Colombo（科伦坡大学）
Agentic AI Software Engineers: Programming with Trust
National University of Singapore（新加坡国立大学）；Carnegie Mellon University, KBR Inc., NASA Ames（卡内基梅隆大学、KBR公司、NASA阿姆斯特朗实验室）；CISPA Helmholtz Center for Information Security, University of Stuttgart（CISPA海姆霍兹信息安全中心、斯图加特大学）；Columbia University（哥伦比亚大学）
Accelerating Manufacturing Scale-Up from Material Discovery Using Agentic Web Navigation and Retrieval-Augmented AI for Process Engineering Schematics Design
Intelligent Design 4.0: Paradigm Evolution Toward the Agentic AI Era
Department of Systems Engineering（系统工程系）；City University of Hong Kong（香港城市大学）；Department of Decision Analytics and Operations（决策分析与运营系）；Department of Information Systems（信息系统系）
