[2605.06737] A Self-Healing Framework for Reliable LLM-Based Autonomous Agents

Computer Science > Software Engineering arXiv:2605.06737 (cs) [Submitted on 7 May 2026] Title:A Self-Healing Framework for Reliable LLM-Based Autonomous Agents Authors:Cheonsu Jeong, Younggun Shin View a PDF of the paper titled A Self-Healing Framework for Reliable LLM-Based Autonomous Agents, by Cheonsu Jeong and 1 other authors View PDF Abstract:Autonomous agents based on Large Language Models (LLMs) are increasingly being utilized in complex software systems. However, reliability remains a significant challenge due to unpredictable failures such as hallucinations, execution errors, and inconsistent reasoning. This paper proposes a reliability-aware self-healing framework for LLM-based software agents. The framework integrates failure detection, reliability assessment, and automated recovery mechanisms. First, we define a taxonomy of failure types and introduce a quantitative reliability assessment model. Next, we propose a failure detection method 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 corrective prompting strategies. The proposed framework was implemented in a multi-agent workflow environment and evaluated using real-world task scenarios. Experimental results demonstrate that our approach significantly increases task success rates, reduces failure propagation, and enhances overall system robustness comp...