Agentic AI for Personalized Trip Planning

Personalized trip planning is becoming increasingly important in modern urban mobility, as travelers expect contextaware, efficient, and adaptive recommendations tailored to their individual needs. This paper investigates how agentic AI can enable such personalization through autonomous reasoning and dynamic itinerary generation. A case study in the Greater Toronto Area demonstrates how specialized agents collaboratively generate persona-specific itineraries while enforcing guardrails for input validation and output verification. Experimental evaluation across multiple large language models (LLMs) and large reasoning models (LRMs) reveals distinct performance tradeoffs: GPT-4 models offer faster responses and concise itineraries suitable for real-time use, while GPT-5 models produce more detailed and consistent plans but with higher latency. The openweight gpt-oss- 120 b achieves stability comparable to GPT5 but with significantly reduced response time, offering a balanced compromise between efficiency and reliability. A composite score integrating response time, itinerary steps, and semantic similarity confirms these findings. Overall, GPT-4 achieves the most balanced performance, GPT-5 favors stability and detail, and gpt-oss-120b provides a strong middle ground. Personalevel analysis shows that accessibility needs and multimodal preferences significantly influence itinerary design, while triplength analysis reveals that journey distance affects itinerary detail and stability. These findings underscore the potential of agentic AI to deliver personalized trip planning experiences.