Complete Cyclic Subtask Graphs for Tool-Using LLM Agents: Flexibility, Cost, and Bottlenecks in Long-Horizon Workflows

Abstract: Long-horizon tool-using tasks sometimes benefit from revisiting earlier subtasks, but explicit revisitation also adds routing, coordination, and token cost. We study complete cyclic subtask graphs for large language model (LLM) agents: a workflow controller in which executable subtasks are fully connected and a unified state-analysis-and-routing agent selects transitions from natural-language criteria. We evaluate task-specific (Spec-Cyc) and benchmark-generic (Gen-Cyc) cyclic graphs on TextCraft, ALFWorld, and Finance-Agent against ReAct and dependency-directed acyclic workflows. The results expose three task regimes. TextCraft behaves like a prerequisite-chain setting, where cyclic routing often adds overhead. ALFWorld behaves like a partially observable recovery setting, where explicit revisitation improves exploration and success. Finance-Agent behaves like an open-ended evidence-synthesis setting, where retrieval, grounding, and synthesis bottlenecks limit all controllers. We add a task-regime selection matrix, fault-injection robustness analysis, token-cost accounting, and scaling discussion. Overall, complete cyclic subtask graphs are best understood as a diagnostic workflow-control tool: they quantify when flexible backtracking is worth its cost and when simpler or sparsified controllers are preferable.

Submission Type: Regular submission (no more than 12 pages of main content)

Assigned Action Editor: ~Tianshu_Yu2

Submission Number: 8792