Do LLM Agents Execute the Plans They Declare? From Planning-Mode Declaration to Pattern-Specific Execution

Large language models (LLMs) enable agents to solve long-horizon tasks by generating a plan and then executing it in an environment. However, successful planning requires two distinct capabilities: selecting an appropriate plan for the task and executing it faithfully. Existing planner–executor systems can fail at either stage: the agent may deviate from a structured plan during execution, or it may faithfully execute a plan that is poorly matched to the task or environment. Final task success alone cannot distinguish between these two sources of failure. We therefore ask: can LLM agents be trusted to execute the plans they commit to, and do different tasks and environments benefit from different planning modes? To study this, we introduce a diagnostic framework for the *Plan Declaration–Execution Gap* and propose *Planning-as-Routing*, in which the LLM declares one of four planning modes: *{Predefined, Sequential, Hierarchical, or Search}*. A deterministic router sends the task to the corresponding pattern-specific executor. Across four benchmarks and three LLMs, we find three consistent patterns. First, generic Plan+ReAct often fails to preserve the declared planning structure, especially for longer plans: across three benchmarks, only \(22\)–\(45%\) of trajectories preserve it, whereas pattern-specific executors enforce the intended structure. Second, planning-mode effectiveness varies across environments and models: Search performs best on ALFWorld, Hierarchical on SWE-bench, and the strongest pattern can vary across models within the same benchmark. Third, the largest gains come from plan execution: pattern-specific executors raise task success from \(0.48\) to \(0.92\) on ALFWorld and from \(0.36\) to \(0.44\) on SWE-bench Verified over Plan+ReAct. However, current LLMs do not reliably select the strongest planning mode for a task, while few-shot examples can improve planning-mode selection for some benchmark–model combinations. Overall, reliable agent planning requires both selecting an effective planning mode and executing it with a matching executor: routing substantially closes the execution gap, while selecting the right mode for each task remains open.