Towards Engineering Multi-Agent LLMs: A Protocol-Driven Approach

The increasing demand for software development has driven interest in automating software engineering (SE) tasks using Large Language Models (LLMs). Recent efforts extend LLMs into multi-agent systems (MAS) that emulate collaborative development workflows, but these systems often fail due to three core deficiencies: under-specification, coordination misalignment, and inappropriate verification, arising from the absence of foundational SE structuring principles. This paper introduces Software Engineering Multi-Agent Protocol (SEMAP), a protocol-layer methodology that instantiates three core SE design principles for multi-agent LLMs: (1) explicit behavioral contract modeling, (2) structured messaging, and (3) lifecycleguided execution with verification, and is implemented atop Google’s Agent-to-Agent (A2A) infrastructure. Empirical evaluation using the Multi-Agent System Failure Taxonomy (MAST) framework demonstrates that SEMAP effectively reduces failures across different SE tasks. In code development, it achieves up to a $69.6 \%$ reduction in total failures for function-level development and $\mathbf{5 6 . 7 \%}$ for deployment-level development. For vulnerability detection, SEMAP reduces failure counts by up to $47.4 \%$ on Python tasks and $28.2 \%$ on $\mathrm{C} / \mathrm{C}++$ tasks.