Execution-first synthetic tool-use trace generation for LLM agents

Agentic systems increasingly operate through executable workflows: they search artifacts, invoke tools, inspect structured observations, and query databases. Training such agents requires supervision that captures valid tool interactions, but query-first synthesis can produce plausible requests that do not correspond to valid tool sequences, compatible parameters, or available data. We propose SyntheticAgentTraceQA, an execution-first framework that constructs workflow templates, assigns tools under data-flow constraints, executes and validates traces, and only then synthesizes user tasks, teacher reasoning, and reference answers. Across the evaluated tool ecosystems, fine-tuning Qwen3.5 variants on the resulting data improves several measures of tool-use behavior, reference-trace agreement, and answer generation. Masked supervision, which excludes reasoning tokens from the loss, yields higher answer-production rates than full supervision and does not consistently improve trace agreement, especially at the 9B scale.