As large language models, and agentic AI systems are increasingly being integrated into software engineering, an expanding amount of empirical evidence surrounding these technologies has emerged. This systematic literature review examines the impact of modern AI techniques and tools in software development lifecycle phases and related activities, covering studies published between 2023 and 2025 and resulting in a corpus of 62 primary studies, investigating the role of large language models, AI agents and agentic AI workflows across lifecycle phases. The synthesis is guided by three research questions that address the entire software development cycle, reported impacts on development practices, outcomes, and constraints. This review fills a gap in the synthesis of modern AI applications and their impacts. It concludes that modern AI in software engineering is progressively evolving from a generation tool into a reasoning and coordination infrastructure layer, with ongoing efforts targeting the mitigation of identified limitations and the advancement of trustworthy agentic AI capabilities for dependable software engineering practices.
Bensaid et al. (Thu,) studied this question.