[Submitted on 18 Feb 2026 (v1), last revised 22 Oct 2026 (this version, v3)]
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.
Submission history
From: Ido Levy [view email]
[v1] Wed, 18 Feb 2026 13:01:17 UTC (1,219 KB)
[v2] Wed, 21 Oct 2026 11:05:33 UTC (828 KB)
[v3] Thu, 22 Oct 2026 17:39:20 UTC (830 KB)
