TripVerse - An AI-Powered Flight Booking and Travel Planning System

Abstract

In the past, travelers were able to use many separate sites to look for flights, book hotels, plan their itineraries, check the weather, and confirm their travels. Decision difficulty, time consumption, and cognitive strain are all increased by this disjointed workflow. Current systems frequently lack intelligent service coordination and offer little automation. TripVerse is a full-stack artificial intelligence (AI) flight booking and travel planning system that uses natural language interaction to provide an integrated and intelligent user experience. To facilitate autonomous coordination among specialized intelligent agents, the platform uses Agentic AI, Agent-to-Agent (A2A) communication, and a structured Message Communication Protocol (MCP). In order to search flights, make reservations, create customized itineraries, and get real-time alerts, users can engage in chat. The system incorporates Google Gemini AI for natural language comprehension, a Weather API for real-time destination forecasts, and a SERP API for real-world data retrieval, including hotels, shopping centers, and tourist attractions. BookingAgent, TripPlannerAgent, NotificationAgent, and DataAgent make up TripVerse\'s multi-agent architecture, which uses MCP for queue-based message exchange. This approach allows for intelligent job delegation while improving fault tolerance, scalability, and modularity. The software also offers administrative dashboards, surveys, travel history tracking, and secure authentication. TripVerse shows how real-world data integration and autonomous AI agents can greatly enhance trip automation, personalization, and operational effectiveness.

Introduction

Modern travel planning involves multiple fragmented tasks—such as flight booking, itinerary creation, accommodation selection, and weather monitoring—often requiring users to switch between several applications, resulting in inefficiency and inconsistent user experiences. Although artificial intelligence has improved conversational interfaces and automation, most existing travel platforms remain centralized, lack autonomous coordination, and offer limited scalability due to the absence of structured inter-agent communication.

To address these challenges, TripVerse is proposed as an AI-powered conversational travel assistant built on an Agentic AI paradigm. The system allows users to issue natural language commands for trip planning and flight booking, which are interpreted by Google Gemini AI. Behind the interface, autonomous agents collaboratively handle itinerary planning, booking, data retrieval, and notification delivery using a standardized Message Communication Protocol (MCP). Real-time data integration through SERP and Weather APIs ensures accurate and up-to-date travel recommendations.

The literature review reveals that most prior systems focus on isolated functionalities such as chatbots or recommendations, with limited automation, weak agent collaboration, and minimal real-time data usage. TripVerse fills these gaps by enabling full end-to-end automation, decentralized agent coordination, and modular system design.

The study’s objectives include developing a scalable AI-driven travel platform, implementing structured multi-agent communication, integrating live data sources, and improving reliability and fault isolation. The system is implemented using a full-stack architecture with Flask, Gemini AI, queue-based agent messaging, and a responsive web interface, supported by an administrative dashboard for monitoring and control.

Evaluation results show that TripVerse achieves over 90% intent recognition accuracy with an average response time below two seconds. The multi-agent architecture improves modularity, coordination efficiency, and system robustness, enabling personalized itineraries and real-time notifications. While current limitations include restricted datasets and the absence of dynamic pricing optimization, the results demonstrate the practical feasibility and effectiveness of agent-based AI for real-world travel automation.

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Conclusion

TripVerse successfully demonstrates the feasibility of deploying Agentic AI for real-world travel automation. The system delivers an integrated, intelligent travel experience through autonomous agent coordination, structured communication, and real-time data integration. It improves usability, scalability, and operational efficiency compared to traditional centralized systems. It is recommended that future deployments incorporate advanced analytics, enhanced security models, and distributed cloud scaling for production-grade applications.

References

[1] Fan, J., Niu, J., Nair, S., Gupta, A., Wu, Y., & Zhang, H. (2024). TravelAgent: Building an AI Agent for Planning Personalized Itineraries. arXiv Preprint arXiv:2412.06874, December 2024. https://arxiv.org/pdf/2412.06874 [2] Nandini, U., Divya, K., Shravani, K., Pratiksha, M., Mayuri, K., & Bhuyar, A. (2023). Smart Travel Booking Application. International Research Journal of Advanced Science and Humanities (IRJASH), 5(5), 145–148. https://www.erpublications.com/uploaded_files/download/ms-nandini-u-ms-divya-k-ms-shravani-k-ms-pratiksha-m-ms-mayuri-k-prof-a-bhuyar_ULxnk.pdf [3] Venkatesh, M. B., & Deshpande, P. K. (2021). AI Based Travel Assistant Using Machine Learning. International Journal of Advanced Research in Computer and Communication Engineering (IJARCCE), 10(5), 193–196, May 2021. https://ijarcce.com/wp-content/uploads/2021/05/IJARCCE.2021.10509.pdf [4] Gupta, S., Sinha, K., Srivastava, S., & Bhandari, K. (2023). AI Based Travel Recommendation System. International Journal of Research Publication and Reviews (IJRPR), 6(3), 300–305. https://ijrpr.com/uploads/V6ISSUE3/IJRPR40122.pdf [5] Mishra, A. S., & Bhalekar, M. H. (2023). AI Based Smart Travel Recommendation System. International Research Journal of Engineering and Technology (IRJET), 12(4), 2435–2439, April 2023. https://www.irjet.net/archives/V12/i4/IRJET-V12I4286.pdf

Copyright

Copyright © 2026 Ms. Dhanashri Lamane, Stalin Rodrigues, Aaron Rodrigues, Mrudul Khandve, Faizan Rizvi. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.