TabAgent: A Framework for Replacing Agentic Generative Components with Tabular-Textual Classifiers

• URL: http://arxiv.org/abs/2602.16429v1
• Date: Wed, 18 Feb 2026 13:01:17 GMT
• Title: TabAgent: A Framework for Replacing Agentic Generative Components with Tabular-Textual Classifiers
• Authors: Ido Levy, Eilam Shapira, Yinon Goldshtein, Avi Yaeli, Nir Mashkif, Segev Shlomov,
• Abstract summary: TabAgent is a framework for replacing generative decision components in closed-set selection tasks with a compact textual-tabular classifier trained on execution traces.<n>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%.
• Score: 5.792704492773729
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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