Reliability Challenges of LLM Agents: Systemic Failure Causes and Governance

Abstract

As Large Language Model (LLM) agents advance toward industrial applications, their systemic reliability challenges become critical obstacles. This review comprehensively analyzes these issues to establish a framework for trustworthy agents, first categorizing key systemic failures into four types: Cognitive-level, Decision-level, Execution-level, and Security & Boundary failures. It then identifies five root causes: training data limitations, model architecture/algorithm defects, imperfect alignment and reinforcement learning, brittle tool/ environment interactions, and vulnerabilities in knowledge representation/update. To address these, the paper proposes a dual-track strategy with a multi-level governance system—emphasizing pre-deployment benchmarks, post-deployment monitoring, and a macro-framework covering multi-agent coordination, interpretability, traceability, responsibility attribution, and full lifecycle risk management—alongside intrinsic enhancement of model factuality, reasoning, alignment, and architecture. This paper aims to provide a systematic perspective and structured approach for building more reliable and responsible LLM agents. It promotes the integration of theoretical safety and practical deployment, driving the advancement of LLM agents from the experimental to the industrial level.