Agentic AI adoption in software development is highest where outputs are verifiable

A new systematic literature review by Spyridon Alvanakis Apostolou, Jan Bosch, and Helena Holmström Olsson analyzes 92 verified studies on agentic AI across the software development lifecycle. The researchers developed a multi-agent screening pipeline using LLMs to handle over 1,600 candidate publications, automating metadata curation and inter-agent dialogue to reduce false negatives. The study follows Kitchenham guidelines and provides a consolidated synthesis of where agentic AI adoption is mature, which architectural patterns dominate, and what limitations and coping mechanisms exist in industrial deployments. The key enabler of adoption is output verifiability — later SDLC phases (e.g., testing, deployment, maintenance) have objectively evaluable outputs through executable feedback, leading to highest industrial maturity. Earlier phases like requirements, design, and architecture remain almost exclusively academic proofs-of-concept. The dominant architectural pattern identified is Planner-Executor-Reviewer role specialization, where the Reviewer agent implements verifiability through executable feedback loops. Industrial mitigation strategies consistently confine agent actions to verifiable, bounded spaces, suggesting that control over agent behavior is critical for production deployment. Future research should extend verification mechanisms to earlier SDLC phases.