A Self-Healing Framework for Reliable LLM-Based Autonomous Agents
Researchers propose a self-healing framework for LLM-based autonomous agents that addresses critical reliability issues including hallucinations, execution errors, and reasoning inconsistencies. The framework combines failure detection, reliability assessment, and automated recovery mechanisms, demonstrating significant improvements in task success rates and system robustness in multi-agent environments.