Autonomous Software Engineering Agents The Next Evolution of AI-Assisted Development

Abstract

The rapid advancement of Artificial Intelligence
has led to the emergence of autonomous software engineering
agents capable of performing complex development tasks with
minimal human intervention. Unlike traditional AI coding assistants, these agents can analyze requirements, generate code,
execute tests, identify defects, and iteratively improve software
solutions through goal-oriented workflows. This paper explores
the architecture, capabilities, and practical applications of autonomous software engineering agents within modern development environments. Particular attention is given to agentbased frameworks, multi-agent collaboration systems, automated
debugging, software maintenance, and continuous integration
pipelines. To ground the discussion empirically, we conduct a
structured synthesis of published experimental evidence on the
SWE-bench family of repository-level benchmarks and on multiagent code-generation frameworks. The synthesized evidence
shows that agentic scaffolding raised real-world GitHub issue
resolution from under 2% for retrieval-augmented baselines
to 12.5% (SWE-agent) and 18–27% on SWE-bench Lite (AutoCodeRover, OpenHands, Agentless), while self-reflective and
multi-agent designs raised function-level synthesis above 85%
pass@1. The study further examines the benefits and limitations
of autonomous agents, including productivity gains, scalability,
decision-making reliability, security concerns—notably indirect
prompt injection—and ethical considerations. The findings indicate that autonomous software engineering agents represent a
significant step toward intelligent software production systems,
where human expertise and artificial intelligence collaborate to
deliver more efficient, reliable, and scalable software solutions.