Agent WARPP: Workflow Adherence via Runtime Parallel Personalization

• URL: http://arxiv.org/abs/2507.19543v1
• Date: Wed, 23 Jul 2025 23:27:49 GMT
• Title: Agent WARPP: Workflow Adherence via Runtime Parallel Personalization
• Authors: Maria Emilia Mazzolenis, Ruirui Zhang,
• Abstract summary: Large language models (LLMs) are increasingly applied in task-oriented dialogue (TOD) systems.<n>We present Adherence via Parallel Personalization, or WARPP, a training-free, modular framework that combines multi-agent runtime with orchestration.<n>By dynamically pruning conditional branches based on user attributes, the framework reduces reasoning overhead and narrows tool selection at runtime.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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: banking, flights, and healthcare. Our evaluation leverages synthetic datasets and LLM-powered simulated users to test scenarios with conditional dependencies. Our results demonstrate that WARPP outperforms both the non-personalized method and the ReAct baseline, achieving increasingly larger gains in parameter fidelity and tool accuracy as intent complexity grows, while also reducing average token usage, without any additional training.

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