Integrating Tool-Guided Planning Subagents into Agent Harnesses as a Tool

While Language Model (LM) agents show remarkable performance on tasks requiring multiple interactions with the environment, their imperfect textual reasoning can produce plans that fail to satisfy task requirements. When environment interactions cannot be freely undone, acting on such plans can lead to irrecoverable failure under limited execution budgets. To mitigate this problem, we propose Tool-guided Planning Subagents (TPS), a plug-and-play planning tool for LM agents' harnesses. Specifically, TPS lets an LM agent delegate planning to subagents through a tool call when needed. It deliberately spends additional internal inference before acting to preserve the environment-execution budget. These subagents use an external solver to select a feasible plan. To provide the solver with an appropriate input, the subagents generate diverse candidate plans, simulate their consequences, and merge shared estimated states into an estimated plan graph. The solver then selects a path satisfying the remaining environment constraints within this graph. The selected plan guides the agent's next actions within its original interaction loop. Under the same environment-action budgets, TPS improves success rates by 38.7 and 25.2 percentage points on average across game and text-world benchmarks, respectively, and improves coding benchmark success by 6.2 points on average. We further demonstrate that TPS improves best mean validation accuracy by 3.5 percentage points in Meta-Harness under the same evaluation budget, extending its application to the harness-design process.