Agent WARPP: Workflow Adherence via Runtime Parallel Personalization
Maria Emilia Mazzolenis, Ruirui Zhang

TL;DR
WARPP is a training-free, modular framework that enhances workflow adherence in LLM-based task-oriented dialogue systems by dynamically personalizing and pruning execution paths at runtime, leading to improved accuracy and efficiency.
Contribution
Introduces WARPP, a novel runtime personalization framework that dynamically tailors workflow execution in LLM systems without additional training, improving adherence and efficiency.
Findings
WARPP outperforms non-personalized and ReAct baselines in accuracy.
Achieves larger gains as intent complexity increases.
Reduces average token usage across tasks.
Abstract
Large language models (LLMs) are increasingly applied in task-oriented dialogue (TOD) systems but often struggle with long, conditional workflows that involve external tool calls and depend on user-specific information. We present Workflow Adherence via Runtime Parallel Personalization, or WARPP, a training-free, modular framework that combines multi-agent orchestration with runtime personalization to improve workflow adherence in LLM-based systems. By dynamically pruning conditional branches based on user attributes, the framework reduces reasoning overhead and narrows tool selection at runtime. WARPP deploys a parallelized architecture where a dedicated Personalizer agent operates alongside modular, domain-specific agents to dynamically tailor execution paths in real time. The framework is evaluated across five representative user intents of varying complexity within three domains:…
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