A travel-tech startup faced an operational challenge when their agentic flight-booking assistant, which initially performed tasks like search, comparison, booking, and itinerary creation seamlessly, began to exhibit subtle inconsistencies due to prompt drift. This drift, characterized by the gradual misalignment between an original prompt's intent and a model's evolving interpretation, led to issues such as misreading travel dates, calling incorrect airline APIs, and stalling mid-booking without clear cause. These changes were not reflected in the system's code or prompts but arose from factors like model updates, evolving user behavior, and tool inconsistencies, making detection difficult. In agentic systems, where multiple data sources and tools are coordinated, even minor shifts in behavior can cascade into broader system failures, resulting in degraded performance and increased support tickets. To manage prompt drift, the text suggests employing LLM observability tools, real-time alerting, and automated prompt optimization, with a focus on Opik's Agent Optimizer, which offers a suite of algorithms to refine prompts and maintain alignment with evolving models and user needs.