ChatSOP: An SOP-Guided MCTS Planning Framework for Controllable LLM Dialogue Agents

ChatSOP introduces a novel framework combining Standard Operating Procedures with Monte Carlo Tree Search to improve controllability of LLM-based dialogue agents. The research demonstrates 27.95% improvement in action accuracy over GPT-3.5 baselines through SOP-guided planning and a curated multi-scenario dialogue dataset.

ChatSOP addresses a fundamental limitation in current LLM dialogue systems: the inability to maintain structured control over conversation flow and task execution. While large language models excel at generating human-like responses, they often diverge from intended objectives or fail to follow procedural constraints—a critical weakness for production dialogue systems in customer service, medical consultation, or enterprise automation. This research tackles the controllability gap by incorporating Standard Operating Procedures as explicit constraints within a Monte Carlo Tree Search planning framework, allowing agents to explore action sequences while respecting operational guidelines.

The technical contribution combines supervised fine-tuning with Chain of Thought reasoning for SOP prediction, enabling models to both understand procedural requirements and plan optimal dialogue paths. The 27.95% accuracy improvement over GPT-3.5 baselines is significant for commercial deployment, suggesting that structured planning methodologies can substantially enhance LLM reliability beyond raw capability improvements. The creation of a semi-automated, manually-validated SOP-annotated dataset addresses a key bottleneck in training controllable agents across diverse scenarios.

For the broader AI industry, this research validates that procedural constraints and planning frameworks enhance LLM utility in structured task domains. Enterprise adoption of dialogue systems has been hampered by reliability and controllability concerns; solutions demonstrating measurable performance improvements on these dimensions have direct commercial relevance. The public release of code and datasets accelerates industry adoption of SOP-guided planning approaches across dialogue applications requiring deterministic behavior.

• →ChatSOP framework improves LLM dialogue agent controllability through SOP-guided Monte Carlo Tree Search planning
• →Achieves 27.95% improvement in action accuracy compared to GPT-3.5 baseline models
• →Combines Chain of Thought reasoning with supervised fine-tuning for enhanced SOP prediction and task execution
• →Curated multi-scenario dialogue dataset with manual quality validation enables training across diverse procedural scenarios
• →Open-source code and datasets facilitate broader adoption of procedural planning approaches in dialogue systems