Multi-Agent Coordination Strategies vs Retrieval-Augmented Generation in LLMs: A Comparative Evaluation

This paper evaluates multi-agent coordination strategies against retrieval-augmented generation for 7-8B open-source models. Four coordination strategies (collaborative, sequential, competitive and hierarchical) were evaluated across three open-source models: Mistral 7B, Llama 3.1 8B and Granite 3.2 8B. The study determined whether multi-agent reasoning enhances retrieval-augmented generation performance. The evaluation employed 100 question-answer pairs. In total, 2,000 model-question evaluations were conducted. Performance was assessed using Composite Performance Score (CPS) and Threshold-aware Composite Performance Score (T-CPS), two metrics developed to aggregate nine dimensions spanning lexical overlap, semantic similarity, and linguistic quality. Results revealed that 87.5% of multi-agent configurations underperformed baseline systems, with coordination overhead identified as the primary limiting factor. Llama 3.1 8B tolerated Sequential and Hierarchical coordination with minimal degradation, while Granite 3.2 8B and Mistral 7B showed severe degradation across all strategies. Collaborative coordination failed universally despite highest output consistency. These findings suggest that single-agent baselines may be preferable for most deployment scenarios under similar conditions. Future research should explore following developments: evaluation of role-specific prompts, investigation of advanced consensus methods, exploration of adaptive systems for strategy selection, and joint tuning of retrieval thresholds and coordination strategies.