Imagine asking a chatbot to schedule a meeting and it goes off on a tangent about the weather. Frustrating, right? That's the problem with today's powerful AI chatbots—they're great at generating human-like text but often lose track of the task at hand. New research tackles this “controllability” challenge with a clever technique called Planning-based Conversational Agents (PCA). Think of it as giving the chatbot a roadmap. Researchers created a system that uses "Standard Operating Procedures" (SOPs) to guide the conversation. These SOPs act like a flowchart, ensuring the chatbot follows specific steps to achieve the desired goal. To teach the AI these procedures, they built a dataset of multi-scenario dialogues, annotated with SOPs. They then trained the chatbot to predict the best course of action by using "Chain of Thought" reasoning and a powerful search algorithm called Monte Carlo Tree Search (MCTS). The results are impressive. The new approach significantly boosted the chatbot's accuracy in completing tasks, like scheduling that meeting without getting sidetracked. This research shows that by combining the flexibility of large language models with the structure of planning algorithms, we can create more reliable and helpful AI assistants. While the current system faces challenges, such as handling unexpected user requests, this work points to a promising future where AI can seamlessly assist us in our daily lives.