Large databases of paper-derived experimental materials data hide essential information on materials. Their analysis is demanding, large-scale and complex, making manual knowledge extraction very difficult. To address this challenge, we developed Multi-Agentic AI (MAA) for automated, autonomous analysis of materials experimental big data and a specialized AI built on the obtained knowledge. MAA's nine role-specific AI agents, under predefined instructions and quality checks, autonomously perform data extraction, compositional feature generation, machine-learning modeling, feature-importance analysis and knowledge-base recording, iterating while inheriting previous knowledge. Delegating numerical processing to code makes large-scale data tractable; recording every choice and result enables later tracing to sources. In four thermoelectric-data experiments varying theme and cycle counts, the knowledge graph grew: knowledge clustered by theme and vocabulary was shared across themes. The specialized AI, searching and reading these records, answered materials questions citing sources. Retrospective verification also revealed errors, traced through the records to their sources.

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