Reliability Assessment in Multi-Agent LLM Systems

Multi-agent systems powered by large language models (LLMs) are transforming enterprise automation. However, systematic evaluation methodologies for assessing tool-use reliability are lacking. A new study introduces a comprehensive diagnostic framework that leverages big data analytics to evaluate procedural reliability in intelligent agent systems, addressing the needs of SMEs in privacy-sensitive environments.

A Data-Driven Diagnostic Approach

The proposed framework includes a 12-category error taxonomy, capturing failure modes in tool initialization, parameter handling, execution, and result interpretation. Through systematic evaluation of 1,980 deterministic test instances, covering both open-weight models (Qwen2.5 series, Functionary) and proprietary alternatives (GPT-4, Claude 3.5/3.7) on diverse edge hardware configurations, reliability thresholds for production deployment have been identified.

Results and Implications

The analysis reveals that procedural reliability, particularly tool initialization failures, is the main bottleneck for smaller models, while Qwen2.5:32b achieves flawless performance, comparable to GPT-4.1. The framework demonstrates that mid-sized models (Qwen2.5:14b) offer practical accuracy-efficiency trade-offs on commodity hardware (96.6% success rate, 7.3 s latency), enabling cost-effective intelligent agent deployments for resource-constrained organizations. This work establishes a foundational infrastructure for systematic reliability evaluation of tool-augmented multi-agent AI systems.