A Self-Healing Framework for Reliable LLM-Based Autonomous Agents

Abstract

Although large language model (LLM)-based autonomous agents are increasingly being utilized in complex software systems, ensuring their reliability remains a critical challenge due to unpredictable defects such as hallucinations, execution errors, and inconsistent reasoning. This study proposes a reliability-aware self-healing framework for LLM-based software agents. This framework integrates failure detection, reliability assessment, and automated recovery mechanisms. First, we define a failure type classification system and introduce a quantitative reliability assessment model. Next, we present a failure detection technique that identifies abnormal agent behavior based on execution patterns and output consistency. Finally, we design a self-healing mechanism that dynamically recovers from failures through adaptive replanning and prompt correction strategies. The proposed framework was tested in a multi-agent workflow environment simulating real-world operational conditions, and its performance was evaluated through task scenarios. The experimental results show that the approach in this study significantly increases the task success rate, reduces fault propagation, and improves the overall robustness of the system compared to existing methods. In particular, this study is distinctive in that it establishes an integrated monitoring system that combines the agents' internal reasoning processes with external execution results. The proposed framework provides a practical foundation for developing trustworthy intelligent agents capable of autonomous failure recovery and adaptive decision-making in dynamic environments. The proposed approach can serve as a core reliability mechanism for next-generation expert systems and autonomous AI applications.