Soil science is entering a new era characterized by the integration of artificial intelligence (AI) multi-agent systems, extending the field beyond traditional machine learning (ML) applications such as digital soil mapping and spectroscopy. While current ML tools are effective for specific tasks, they often lack the reasoning, contextual integration, and adaptability required to address complex, dynamic soil systems. We propose multi-agent AI systems—autonomous, interactive software agents capable of perceptual processing, planning, and scientific reasoning—as a novel framework to support and accelerate soil science research. These agents can fulfill diverse roles, including synthesizing data from field sensors and remote sensing to create dynamic digital soil twins, generating hypotheses, designing experiments, and simulating climate-driven changes in soil function. To illustrate this approach, we tasked a multi-agent system with creating research hypotheses on the topic of mineral-associated organic carbon saturation in soils. The agents generated five hypotheses on effective versus theoretical saturation thresholds, biological and chemical controls, climate influence, interdisciplinary feedback, and actionable management strategies. Each hypothesis was evaluated for empirical grounding, conceptual breadth, and scientific rigor by experts and a simulated peer review. Our findings highlight the potential of multi-agent AI systems, guided by human experts, to accelerate early-stage discovery, support interdisciplinary exploration, and emulate the scientific review process. Nonetheless, challenges remain, particularly around data quality, model transparency, epistemic overtrust, computational cost, ethical implications, and the retention of foundational scientific knowledge. We emphasize AI as an augmentative partner, not a replacement, for human-led discovery.