TabAgent: Distilling LLM Agent Components into Cheap Classifiers
TABAGENT replaces slow, expensive large language model calls used for agent decision-making with a compact textual-tabular classifier trained on execution traces. It extracts structured features like schema and state from agent actions, generates synthetic data to improve training coverage, and uses these features to score candidates. This method maintains high task success while drastically reducing latency by about 95% and inference costs by 85-91%.