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A Multiple Case Study of Artificial Intelligent System Development in Industry
There is a rapidly increasing amount of Artificial Intelligence (AI) systems developed in recent years, with much expectation on its capacity of innovation and business value generation. However, the promised value of AI systems in specific business contexts might not be understood, and further integrated into the development processes. We wanted to understand how software engineering processes and practices can be applied to develop AI systems in a fast-faced, business-driven manner. As the first step, we explored contextual factors of AI development and the connections between AI developments to business opportunities. We conducted 12 semi-structured interviews in seven companies in Brazil, Norway and Southeast Asia. Our investigation revealed different types of AI systems and different AI development approaches. However, it is common that business opportunities involving with AI systems are not validated and there is lack of business-driven metrics that guide the development of AI systems. The findings have implications for future research on business-driven AI development and supporting tools and practices.
Experimenting with Multi-Agent Software Development: Towards a Unified Platform
Large language models are redefining software engineering by implementing AI-powered techniques throughout the whole software development process, including requirement gathering, software architecture, code generation, testing, and deployment. However, it is still difficult to develop a cohesive platform that consistently produces the best outcomes across all stages. The objective of this study is to develop a unified platform that utilizes multiple artificial intelligence agents to automate the process of transforming user requirements into well-organized deliverables. These deliverables include user stories, prioritization, and UML sequence diagrams, along with the modular approach to APIs, unit tests, and end-to-end tests. Additionally, the platform will organize tasks, perform security and compliance, and suggest design patterns and improvements for non-functional requirements. We allow users to control and manage each phase according to their preferences. In addition, the platform provides security and compliance checks following European standards and proposes design optimizations. We use multiple models, such as GPT-3.5, GPT-4, and Llama3 to enable to generation of modular code as per user choice. The research also highlights the limitations and future research discussions to overall improve the software development life cycle. The source code for our uniform platform is hosted on GitHub, enabling additional experimentation and supporting both research and practical uses. \end
Bridging Humans and LLMs: Investigating Human-AI Collaboration in Multi-agent Requirements Analysis for Organizational AI Adoption
Context: Organizations adopting Artificial Intelligence (AI) face challenges in eliciting and analyzing requirements that align with strategic objectives, especially when human oversight and iterative refinement are needed. Large Language Models (LLMs)-based Multi-agent systems provide a potential solution by supporting structured and collaborative Requirements Engineering (RE) processes for AI adoption planning. Objective: The objective of this study is to investigate whether a multi-agent system, built on LLMs and supported by human input, can assist in requirements analysis for AI adoption. Method: We used a mixed-method approach: (i) designed and developed a multi-agent system to support the generation and prioritization of requirements for AI adoption, (ii) conducted multiple case studies with four companies to evaluate the system, and (iii) collected data through post-session questionnaires from nine participants and follow-up interviews, one per company. Results: Questionnaire and interview findings together indicate that the system may assist in identifying relevant and goal-aligned requirements. Seven participants considered the generated requirements relevant, and six found them aligned with organizational goals. Participants noted that iterative feedback improved completeness and feasibility, often within two feedback rounds. Both data sources show that human input was essential to clarify technical details, ensure contextual accuracy, and validate prioritization results. Participants from all companies also identified usability, transparency, and scalability as areas requiring further refinement for broader organizational use. Conclusions: LLM-based multi-agent systems can support strategic AI planning by enabling iterative refinement with human experts. Future work will include more interviews with stakeholders and adjustments to system features to improve transparency, usability, and scalability.
Computational and AI-Driven Ecosystem for Structure-Based Covalent Drug Discovery.
ConspectusThe field of covalent drug discovery has witnessed a remarkable resurgence in recent years, a trend underscored by the approval of more than 125 covalent drugs by the US FDA as of 2025, which demonstrates their immense therapeutic potential. Driven by ever-increasing computational power and vast amounts of data, deep learning (DL) is profoundly transforming numerous fields, from natural language processing to drug discovery. In the development of covalent drugs, in particular, advanced computational methods centered on data-driven approaches and artificial intelligence (AI) exhibit immense potential. The realization of this potential depends on the construction of a synergistic ecosystem. Here, we define this "ecosystem" as an integrated set of components─including (i) curated covalent-relevant databases, (ii) AI/physics-based predictive and scoring models, (iii) interoperable computational workflows spanning site identification, docking/virtual screening, and lead optimization, and (iv) closed-loop feedback that systematically incorporates experimental outcomes to update data resources and refine/validate models. This begins with the systematic collection of past experimental results to build high-quality databases. These databases, in turn, provide the foundation for developing AI-driven computational tools capable of precisely interfacing with and accelerating downstream tasks, such as molecular docking (for generating physically plausible conformations and conducting large-scale virtual screening) and lead optimization. The application of these AI tools not only guides experimental design, but the resulting key data also feed back into and enrich the databases. Furthermore, in the cutting-edge field of covalent drugs, the precise identification of "druggable" covalent sites on target proteins has emerged as another critically important downstream task.In this Account, we describe a computational and AI-driven ecosystem for structure-based covalent drug discovery and highlight our contributions to this field. By explicitly linking databases, models, workflows, and experimental feedback into a single framework, this Account moves beyond a simple inventory of individual tools to instead offer a systematic and panoramic perspective on an integrated ecosystem for covalent drug discovery, driven by data and computational engines including AI. We focus on how this ecosystem systematically addresses the challenges from covalent binding site identification to lead discovery, thereby fundamentally accelerating the development of next-generation covalent therapies. We first articulate the philosophy behind the construction and updating of covalent databases, emphasizing the necessity of high-quality data. Subsequently, we delve into a suite of cutting-edge, AI-driven computational methods, exploring the potential of deep learning in tasks such as molecular docking, covalent binding site prediction, and lead optimization. To bridge the gap between computational theory and experimental validation, we will use the discovery of potent covalent CRM1 inhibitors as a specific case study, detailing how our customized, structure-based virtual screening pipeline was utilized to achieve a seamless workflow from computational prediction to biological validation. This section is intended to offer actionable guidance for experimental researchers seeking to leverage these powerful computational tools. Finally, we highlight the limitations and potential pitfalls of this AI engine─concerns that are equally relevant when developing AI-driven covalent docking algorithms. Building on our group's recent benchmarking of AI docking methods, we objectively evaluate current performance and discuss how transformative advances such as AlphaFold3 may reshape the field.