Towards Full Delegation: Designing Ideal Agentic Behaviors for Travel Planning

Song Jiang, Da JU, Andrew Cohen, Sasha Mitts, Aaron Foss, Justine T Kao, Xian Li, Yuandong Tian

Keywords: Agentic Behavior, LLMs, LLM-as-a-Judge, Synthetic Data

TL;DR: We propose APEC Agent Constitution, a list of principles for good agentic behaviors. Following this, we develop APEC-Travel, a travel planning agent that proactively extracts hidden personalized needs via multi-round dialog with travelers.

Abstract: How are LLM-based agents used in the future? While many of the existing work on agents has focused on improving the performance of a specific family of objective and challenging tasks, in this work, we take a different perspective by thinking about full delegation: agents take over humans’ routine decision-making processes and are trusted by humans to find solutions that fit people’s personalized needs and are adaptive to ever-changing context. In order to achieve such a goal, the behavior of the agents, i.e., agentic behaviors, should be evaluated not only on their achievements (i.e., outcome evaluation), but also how they achieved that (i.e., procedure evaluation). For this, we propose APEC Agent Constitution, a list of criteria that an agent should follow for good agentic behaviors, including Accuracy, Proactivity, Efficiency and Credibility. To verify whether APEC aligns with human preferences, we develop APEC-Travel, a travel planning agent that proactively extracts hidden personalized needs via multi-round dialog with travelers. APEC-Travel is constructed purely from synthetic data generated by Llama3.1-405B-Instruct with a diverse set of travelers’ persona to simulate rich distribution of dialogs. Iteratively fine-tuned to follow APEC Agent Constitution, APEC-Travel surpasses baselines by 20.7% on rule based metrics and 9.1% on LLM-as-a-Judge scores across the constitution axes.

Submission Number: 130