AI agents are increasingly capable of handling complex, real-world tasks, but integrating them into production systems to manage asynchronous workflows remains a challenge. Durable execution, a concept introduced to enhance AI agent reliability, enables these systems to interact robustly with external tools, maintaining fault tolerance and persistence even during failures. This approach is exemplified in a refund process AI agent, which can autonomously manage tasks such as processing refunds asynchronously while involving humans in decision-making as needed. By applying durable execution principles, the agent ensures that workflows remain uninterrupted and that tasks are neither duplicated nor lost, thereby enhancing the reliability of AI automation. The integration of frameworks like DBOS with popular AI systems such as LangGraph allows for seamless orchestration of complex workflows within application code, without needing external orchestrators or job queues. This synergy not only simplifies the development of durable AI agents but also improves observability and state management, ensuring that customer service operations reliant on AI are efficient and resilient.