ODFuse: Observation-Dependency-Guided Step Fusion for Efficient Tool-Use Agents

A multi-step tool-use LLM agent must end a step before any operation with an observation dependency: it cannot determine how it uses an intermediate result until it observes that result. Existing agents such as vanilla CodeAct satisfy this dependency only implicitly: they stop after every short block of code and pay for a new round of model reasoning at every stop. Fusing regardless removes those stops, but the agent then acts on evidence it has not seen. We introduce ODFuse, a prompt-only extension of CodeAct agents that constructs each execution step according to observation dependency: it fuses observation-resolved operations and defers unresolved ones until their required evidence has been observed. On two popular tool-use benchmarks (AppWorld and MCP-Universe) with four backbones, ODFuse takes 27–78% fewer steps than vanilla CodeAct, which cuts per-task cost by up to 50% in most settings at a limited loss of accuracy. It also commits 95% fewer over-fusions than an agent that fuses regardless, on the benchmark where that agent errs most.