MAS-FIRE: Fault Injection and Reliability Evaluation for LLM-Based Multi-Agent Systems

TL;DR

MAS-FIRE introduces a comprehensive fault injection framework to evaluate and diagnose the reliability of LLM-based multi-agent systems, revealing insights into fault tolerance and the impact of architecture and model strength.

Contribution

This paper presents MAS-FIRE, a novel systematic framework for fault injection and detailed reliability analysis of multi-agent systems using large language models.

Findings

Stronger models do not always enhance robustness.

Architectural topology significantly influences fault tolerance.

Iterative, closed-loop designs mitigate over 40% of catastrophic faults.

Abstract

As LLM-based Multi-Agent Systems (MAS) are increasingly deployed for complex tasks, ensuring their reliability has become a pressing challenge. Since MAS coordinate through unstructured natural language rather than rigid protocols, they are prone to semantic failures (e.g., hallucinations, misinterpreted instructions, and reasoning drift) that propagate silently without raising runtime exceptions. Prevailing evaluation approaches, which measure only end-to-end task success, offer limited insight into how these failures arise or how effectively agents recover from them. To bridge this gap, we propose MAS-FIRE, a systematic framework for fault injection and reliability evaluation of MAS. We define a taxonomy of 15 fault types covering intra-agent cognitive errors and inter-agent coordination failures, and inject them via three non-invasive mechanisms: prompt modification, response rewriting, and message routing manipulation. Applying MAS-FIRE to three representative MAS architectures, we uncover a rich set of fault-tolerant behaviors that we organize into four tiers: mechanism, rule, prompt, and reasoning. This tiered view enables fine-grained diagnosis of where and why systems succeed or fail. Our findings reveal that stronger foundation models do not uniformly improve robustness. We further show that architectural topology plays an equally decisive role, with iterative, closed-loop designs neutralizing over 40% of faults that cause catastrophic collapse in linear workflows. MAS-FIRE provides the process-level observability and actionable guidance needed to systematically improve multi-agent systems.