Aug 2026· Proceedings of the 32nd ACM SIGKDD Conference on Knowledge Discovery and Data Mining V.2· pp. 13417-13418· 0 citations
Abstract
We are witnessing the emergence of Agentic Software Engineering (SE~3.0), where AI agents act as autonomous AI Teammates performing complex tasks such as coding, debugging, and testing. As AI Teammates generate a vast new category of digital artifacts, they introduce unique opportunities and challenges related to human-AI collaboration, trustworthiness, and economic impact. This workshop serves as the premier forum for addressing these challenges, anchored by the launch of the AIDev dataset. Comprising over one million agentic pull requests generated by AI Teammates such as Claude Code, OpenAI Codex, and GitHub Copilot, AIDev provides the empirical evidence needed to understand the behaviors of AI Teammates. This workshop features insights from major industry players and academic pioneers, and aims to define a roadmap for a world where AI Teammates and human developers build the future together.
Context: Software engineering is moving from AI-assisted practices like vibe coding, in which assistants accelerate individual developers, towards Agentic Software Engineering (ASE), in which autonomous agents are delegated goal-level tasks. However, industry reports a productivity paradox: as individual productivity increases, team throughput, review capacity, and stability degrade because team-scale software engineering discipline is neglected. Objective: This paper aims to establish the conceptual and methodological foundations of Spec-Driven Development (SDD) as an enabling discipline for ASE at team scale and characterize the harness, i.e., the technical and methodological mechanisms through which teams govern agent behavior. Method: We conducted a conceptual analysis drawing predominantly on gray literature, including ASE vision and roadmap papers, practitioner reports, talks, and tooling, because peer-reviewed evidence and a shared academic-industrial vocabulary are not yet established. Results: Using a comparative characterization of the paradigm progression as conceptual framing, the article presents (i) a socio-technical model of SDD in which specifications act as the contract substrate between humans and agents; (ii) an operational characterization of the harness, distinguishing the technical harness around the agent from the methodological harness around the team, with worked examples; and (iii) a typology of five human--agent interaction patterns through which the human role is redefined. Conclusion: We conclude that SDD reconstitutes, in specification-centric form, the contracts that vibe coding dissolves: accountability, verifiability, and transferability. Given the immaturity of the evidence base, this work is presented as a first step toward academic-industrial consensus rather than a validated theory, and outlines a research agenda for future empirical validation.
J. Díaz, J. Gayoso, Andrea Cimminio et al.· 0 citations
AI agent systems increasingly support software engineering by extending large language models with capabilities such as planning, tool use, and coordinated execution, yet empirical evidence on the engineering challenges of building and maintaining such frameworks remains limited. To fill this gap, we conduct a large-scale empirical study of 3,864 closed GitHub issues from three representative repositories. We present a taxonomy of engineering challenges comprising 5 top-level categories and 21 subcategories, analyze the popularity and difficulty of these categories, and summarize 47 actionable solution strategies from resolved issue discussions and linked pull requests. These findings provide practical guidance for developers and framework providers, and offer an empirical basis for future research on AI agent engineering.
Chen Liu, Xunhui Zhang, Tao Wang· Fall Joint Computer Conferen...· 0 citations
A comprehensive overview of the existing tools and frameworks for implementing MAS in software engineering and a set of lessons learned and challenges that can help researchers and practitioners to select a suitable MAS framework according to their needs are provided.
Maria Sâmyla Serafim de Oliveira, M. Ibiyo, Marco Gianrusso et al.· 0 citations
Overall, repository-preserved Agent Plans under these tool-specific directories appear to be a narrow but informative artifact for studying task intent and execution guidance in human-agent workflows.
M. Abubakar, Seyedmoein Mohsenimofidi, Jai Lal Lulla et al.· 1 citation
This note argues that a skill is a software artefact and that its construction should follow software-engineering principles, with qualifications: single responsibility, separation of interface from implementation, low coupling, and economy in a shared token budget, together with behavioural evaluation in place of deterministic testing.
Giuseppe Destefanis· arXiv.org· 1 citation
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