From Intelligence to Delegation in Human-Agent Teams in From Intelligence to Delegation in Human-Agent Teams in Software Development Software Development
This study examines the mechanisms through which developers’ perceptions of AI agent intelligence shape their trust in AI teammates and, in turn, influence delegation behavior, and proposes a moderate mediation model in which these relationships are moderated by three factors.
SDD reconstitutes, in specification-centric form, the contracts that vibe coding dissolves: accountability, verifiability, and transferability, and outlines a research agenda for future empirical validation.
J. Díaz, J. Gayoso, Andrea Cimminio et al.· 4 citations· ⚡1
As AI coding agents carry out development tasks with greater autonomy, developers are shifting from direct implementation toward supervising delegated work. Yet existing research offers limited understanding of how developers organize supervisory activities into connected workflows. Drawing on observations and workflow...
Large language models become consequential agents when surrounding systems let outputs change external state. Models now call tools, operate interfaces, delegate work, retain state, inhabit generated worlds, and control robots or laboratory equipment. Such advances are often narrated as one march toward autonomy, confl...
AgentSWE is introduced, a benchmark for agent software engineering spanning three stages of the agent development lifecycle: Creation of a complete agent from a natural-language requirement, Editing of production agent codebases, and Optimization toward a measurable target.
Jia-Hao Wang, Hong-Jin Qian, Yuyang Hu et al.· 0 citations
MAGE explains how externalized knowledge, bounded action, independent evaluation, and retained human authority can compose into a governed engineering environment, and proposes tests of when that environment turns commodity intelligence into durable engineering progress.
James C. Davis, Kelechi G. Kalu, Hui-Yun Peng et al.· 2 citations
Artificial intelligence has evolved from rule-based symbolic systems into learning architectures capable of perception, prediction, content generation, reasoning, planning, tool use, and increasingly autonomous action. This qualitative literature review examines this transition as more than a technological chronology....
Loso Judijanto· Journal of Engineering and A...· 0 citations
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