Agentic systems, AI architectures that autonomously execute multi-step workflows to achieve complex goals, are often built using repeated large language model (LLM) calls for closed-set decision tasks such as routing, shortlisting, gating, and verification. While convenient, this design makes deployments slow and expensive due to cumulative latency and token usage. We propose TabAgent, a framework for replacing generative decision components in closed-set selection tasks with a compact textual-tabular classifier trained on execution traces. TabAgent (i) extracts structured schema, state, and dependency features from trajectories (TabSchema), (ii) augments coverage with schema-aligned synthetic supervision (TabSynth), and (iii) scores candidates with a lightweight classifier (TabHead). On the long-horizon AppWorld benchmark, TabAgent maintains task-level success while eliminating shortlist-time LLM calls, reducing latency by approximately 95% and inference cost by 85-91%. Beyond tool shortlisting, TabAgent generalizes to other agentic decision heads, establishing a paradigm for learned discriminative replacements of generative bottlenecks in production agent architectures.

翻译：智能体系统，即能够自主执行多步骤工作流以实现复杂目标的人工智能架构，通常通过重复调用大语言模型（LLM）来完成封闭集合决策任务，例如路由、候选列表筛选、门控和验证。尽管这种设计很方便，但由于累积延迟和令牌使用量，它使得部署变得缓慢且昂贵。我们提出了TabAgent，这是一个用于在封闭集合选择任务中用经过执行轨迹训练的紧凑型文本-表格分类器替代生成式决策组件的框架。TabAgent（i）从轨迹中提取结构化模式、状态和依赖特征（TabSchema），（ii）通过模式对齐的合成监督增强覆盖范围（TabSynth），以及（iii）使用轻量级分类器对候选进行评分（TabHead）。在长视野AppWorld基准测试中，TabAgent在保持任务级成功率的同时，消除了候选列表筛选阶段的LLM调用，将延迟降低了约95%，并将推理成本降低了85-91%。除了工具候选列表筛选，TabAgent还可推广到其他智能体决策头，为生产智能体架构中生成式瓶颈的学习型判别式替代方案确立了一种范式。