Choosing and Operating Tabular Models Inside AI Agents

AI agents that make decisions over structured data rely heavily on tabular learning models — but model choice has direct implications for agent reliability, routing, and operational behavior. In this benchmark, we evaluate 7 widely used tabular model families across 19 real-world datasets (~260k rows, 250+ features) to understand which models agents should invoke under different data regimes. Rather than focusing solely on average rank, we analyze win rates to capture dominance — a critical signal when agents must choose models dynamically at runtime. The results reveal that: Foundation models are most effective for agents operating with limited data XGBoost is the most reliable choice for large, numeric-heavy workloads Hybrid datasets at scale remain operationally ambiguous, with multiple viable model choices These findings highlight a core Agent Ops challenge: model selection and routing inside agents is a runtime decision, not a one-time architecture choice. As agents increasingly combine LLM reasoning with structured prediction, understanding the operational strengths and failure modes of tabular models becomes essential for building robust, cost-aware agent systems.