Suraj Agrawal

Sumanth Balaji, Piyush Mishra, Aashraya Sachdeva et al.

Traditional customer support systems, such as Interactive Voice Response (IVR), rely on rigid scripts and lack the flexibility required for handling complex, policy-driven tasks. While large language model (LLM) agents offer a promising alternative, evaluating their ability to act in accordance with business rules and real-world support workflows remains an open challenge. Existing benchmarks primarily focus on tool usage or task completion, overlooking an agent's capacity to adhere to multi-step policies, navigate task dependencies, and remain robust to unpredictable user or environment behavior. In this work, we introduce JourneyBench, a benchmark designed to assess policy-aware agents in customer support. JourneyBench leverages graph representations to generate diverse, realistic support scenarios and proposes the User Journey Coverage Score, a novel metric to measure policy adherence. We evaluate multiple state-of-the-art LLMs using two agent designs: a Static-Prompt Agent (SPA) and a Dynamic-Prompt Agent (DPA) that explicitly models policy control. Across 703 conversations in three domains, we show that DPA significantly boosts policy adherence, even allowing smaller models like GPT-4o-mini to outperform more capable ones like GPT-4o. Our findings demonstrate the importance of structured orchestration and establish JourneyBench as a critical resource to advance AI-driven customer support beyond IVR-era limitations.

Suraj Agrawal, Dwaipayan Roy, Mandar Mitra

Personalized Point of Interest recommendation is very helpful for satisfying users' needs at new places. In this article, we propose a tag embedding based method for Personalized Recommendation of Point Of Interest. We model the relationship between tags corresponding to Point Of Interest. The model provides representative embedding corresponds to a tag in a way that related tags will be closer. We model Point of Interest-based on tag embedding and also model the users (user profile) based on the Point Of Interest rated by them. finally, we rank the user's candidate Point Of Interest based on cosine similarity between user's embedding and Point of Interest's embedding. Further, we find the parameters required to model user by discrete optimizing over different measures (like ndcg@5, MRR, ...). We also analyze the result while considering the same parameters for all users and individual parameters for each user. Along with it we also analyze the effect on the result while changing the dataset to model the relationship between tags. Our method also minimizes the privacy leak issue. We used TREC Contextual Suggestion 2016 Phase 2 dataset and have significant improvement over all the measures on the state of the art method. It improves ndcg@5 by 12.8%, p@5 by 4.3%, and MRR by 7.8%, which shows the effectiveness of the method.