Debugging AI agents, particularly those powered by Retrieval-Augmented Generation (RAG), poses unique challenges due to their complex decision-making processes and adaptive behaviors, which often result in issues like hallucinations and unpredictable outputs. Unlike traditional chatbots, AI agents autonomously perform tasks and make decisions based on data from varied sources, which makes debugging difficult without insights into their internal states. The text highlights the importance of session tracking and the use of tools like Helicone's Sessions to trace multi-step processes, identify errors, and fine-tune agent responses. Examples of common issues are illustrated with scenarios involving travel, health, and educational chatbots, where session logs and debugging tools are used to correct errors such as misinterpreted user inputs and inconsistent recommendations. The article emphasizes the need for specialized debugging tools, such as Helicone, AgentOps, and Portkey, to provide visibility into AI agents' workflows, thereby enhancing their reliability and accuracy for production readiness across various applications.