Assistance to Autonomy: A Systematic Literature Review of Agentic AI across the Software Development Life Cycle

TL;DR

This systematic review analyzes the adoption, architectural patterns, and limitations of agentic AI in software development, highlighting the importance of verifiability and bounded actions for industrial maturity.

Contribution

It provides a comprehensive synthesis of agentic AI in SDLC, introduces a novel multi-agent screening pipeline, and identifies key architectural and deployment insights.

Findings

Output verifiability enables higher maturity and industrial adoption.

Planner-Executor-Reviewer is the dominant architectural pattern.

Industrial mitigation strategies focus on bounded, verifiable agent actions.

Abstract

Agentic AI in software product development is increasingly adopted by organizations, yet the field lacks a consolidated synthesis of where adoption is mature, which architectural patterns dominate, and what limitations and coping mechanisms exist in industrial deployments. This systematic literature review addresses these gaps by establishing a body of knowledge as a starting point. Following Kitchenham guidelines, we queried four major research databases, obtaining over 1600 candidate publications. To handle this volume, we developed and validated a domain-agnostic multi-agent screening pipeline that extends prior LLM-assisted review tools by combining automatic metadata curation, inter-agent iterative dialogue, and conflict-resolution defaults that minimize false negatives. From the 92 manually verified primary studies, our thematic synthesis reveals that output verifiability is the primary enabler of agentic adoption: later SDLC phases, whose outputs are objectively evaluable through executable feedback, demonstrate the highest maturity and industrial presence, while earlier phases remain almost exclusively academic proofs-of-concept. We identify the Planner-Executor-Reviewer role specialization as the dominant architectural pattern, with the Reviewer agent implementing verifiability through executable feedback loops. Across all challenge categories, industrial mitigation strategies converge on confining agent actions to verifiable, bounded spaces. This study contributes a comprehensive characterization of the current literature on agentic systems in software product development, and a methodological contribution in the form of an AI-assisted tool to automate the screening phase in high-volume SLR domains.