# Capability-Grounded Agentic Planning

LLM agents usually pick tool calls by trial and error, with no guarantee that a plan can execute. CGAP compiles the tool catalog deterministically into an abstract PDDL model that tracks only which fields are available, lets a classical planner order the calls, and has an LLM add entity-specific calls whose arguments and results are checked. Across ToolSandbox, ComplexFuncBench, BFCL multi-turn and τ-bench retail with three LLMs, it beats a standard tool-calling agent, by up to 28.9 points.

LLM agents usually pick tool calls by trial and error, with no guarantee that a plan can execute. CGAP compiles the tool catalog deterministically into an abstract PDDL model that tracks only which fields are available, lets a classical planner order the calls, and has an LLM add entity-specific calls whose arguments and results are checked. Across ToolSandbox, ComplexFuncBench, BFCL multi-turn and τ-bench retail with three LLMs, it beats a standard tool-calling agent, by up to 28.9 points.