AI Summary: This comprehensive review paper maps the integration of multi-agent AI systems across the complete software development lifecycle (SDLC). It illustrates that agentic AI has moved beyond simple code autocomplete (like early Copilots) into autonomous requirements gathering, architectural planning, and robust debugging.
This concept paper systematically reviews the emerging paradigm of LLM-based multi-agent systems, examining their applications across the entire Software Development Life Cycle (SDLC), from requirements engineering and code generation to static code checking, testing, and debugging. We analyze how autonomous agents are transitioning from mere coding assistants to active participants that orchestrate complex engineering workflows. Furthermore, we identify key challenges and outline future research opportunities, with a focus on multi-agent orchestration, human-agent coordination, computational cost optimization, and effective data collection.
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