AI agents promise to turn scattered scientific evidence into AI-ready datasets, yet existing benchmarks examine isolated NLP skills or paper-level summarization and leave open whether agents can assemble complete, evidence-grounded records across multimodal and heterogeneous scientific sources. To address this gap, we introduce ABDataBench, a general evaluation protocol for antibody functional data construction from scientific literature and patents. ABDataBench contains 44 expert-annotated, OCR-processed documents, including 27 papers and 17 patents, yielding 279 antibody-level records under a unified 22-field schema. We further develop ABCurator, a record-centric multi-agent framework that treats each antibody as the unit for planning, evidence aggregation, enrichment, and validation, coupled with a skill-accumulation workflow that distills expert feedback into reusable agent competencies across evaluation rounds. Across proprietary and open-source models, the strongest configurations achieve scores of 78.3 and 76.0, respectively. Analysis shows that current agents achieve high antibody recall but remain limited by over-generation, sequence attribution errors, and incomplete affinity grounding. These results position antibody record construction as a challenging long-horizon information-extraction task and establish ABDataBench as a benchmark framework for reliable AI-driven scientific data generation. The source code and data for ABDataBench is released at:https://github.com/GAIR-NLP/ABDataBench.