In advanced systems built with LLM-based agents, failures can occur in various forms, such as prompt hallucinations, context window overflowing, semantic drift, and inconsistencies in responses. This presentation demonstrates the practical implementations of self-healing methods aimed at LLM agent architectures with Python. Based on production knowledge, we illustrate how to build a three-tiered detection and healing system: preventative (prompt and context optimization), detective (monitoring system performance and checking semantic consistency), and corrective (automated agent re-initialization and prompt refinement). We will demonstrate the creation of ‘agents’ capable of self-monitoring cognitive coherence, semantic degradation, and autonomously recovering from a multitude of failures when LangChain, CrewAI, and bespoke monitoring systems are used. Participants will gain skills in building complete self-healing functionalities to ensure reliable LLM-based agent systems in production.