Compiling Mixed Agent Traces into Guarded Executable Workflows

LLM agents often solve repeated tasks by reconstructing similar tool-use proce- dures with different inputs. Although storing reusable local functions can reduce low-level execution effort, the model still needs to reason about how to compose these functions for every new request. We propose Trace2Workflow, a framework that converts successful interaction traces into reusable executable workflows. In- stead of repeatedly planning from scratch, the agent can directly reuse a learned workflow when its applicability conditions are satisfied. This reduces the reason- ing burden of the model and avoids unnecessary LLM calls. When no workflow is applicable, the agent falls back to normal model-based reasoning, which pre- serves robustness on unseen tasks. We evaluate Trace2Workflow on ALFWorld and AppWorld under a continual setting. We compile previously successful task- level compositions, rather than only local skills, into guarded executable work- flows whose bindings and observable effects are checked at runtime. These re- sults show that reusable workflows can amortize repeated composition reasoning across tasks, reducing both model inference burden and execution cost without sacrificing task performance.