As artificial intelligence becomes increasingly embedded in scholarly practice, its role within the humanities requires both methodological clarity and critical scrutiny. This paper presents a practical, multi-agent workflow for integrating generative AI into historical research while maintaining rigorous academic standards. Drawing on the author’s use of DeepSeek for factual retrieval, ChatGPT for dialogic interpretation and narrative synthesis, and ClaudeAI for structural review, the study demonstrates how different models can function as complementary counterparts—mirroring the distributed expertise of peer review. Through case studies—including the development of a quantitative framework for assessing household wealth in Pompeii, the reconstruction of post-catastrophe cultural cycles in Mesoamerica, and the reinterpretation of ancient Mediterranean artifacts—the paper illustrates how iterative questioning enables AI to operate as an intellectual partner rather than a passive search tool. The analysis also highlights the risks inherent in relying on opaque training models, such as citation fabrication, semantic drift, and uncritical reinforcement of user assumptions. To mitigate these challenges, the paper outlines verification protocols grounded in cross-checking with authoritative databases such as WorldCat, Google Scholar, and JSTOR, and discusses the use of generative image tools (Adobe Firefly, DALL·E) to create historically informed visualizations while maintaining ethical and evidentiary standards. Ultimately, the study argues that AI can significantly amplify humanistic inquiry—expanding interdisciplinary reach, accelerating interpretive insight, and supporting the construction of deeper historical understanding—provided scholars remain vigilant stewards of evidence, provenance, and context.