Building Real-Time Multi-Agent AI for Public Travel Systems

About

RAHAT (Responsive AI Helper and Tasker) is a multi-agent AI system designed to assist railway and airport passengers by providing intelligent, real-time responses to various travel-related queries. From getting live train status, platform details, and ticket waitlist information to locating station facilities and calling for emergency assistance, RAHAT leverages LLM-based agents and tool integration to simulate smart, interactive terminals. With built-in memory, voice support (optional), and the potential for hardware integration (e.g., kiosks or mobile bots), RAHAT redefines the way public information is accessed and services are delivered.

Key Takeaways:

  • Agentic Systems Go Beyond Chatbots: Multi-agent architectures simulate teamwork, delegate tasks, and take actions.
  • Tool Integration is the Real Gamechanger: LLM agents combined with tools (functions/APIs) make AI useful for task execution.
  • Modular Design Enables Domain Specialization: Agents like Railway Agent and Emergency Agent can be built and scaled independently.
  • Memory Makes AI Feel Personal: Personalized experiences through short-term memory modules
  • Real-World Deployment Ready: Connect to APIs like IRCTC, DGCA, or Smart City Portals.
  • Hardware + Software = Smarter BOTS for the Common Man: Embedded in terminals with mic/speaker for rural and elderly accessibility.
  • AI for Traditional Systems – A New Lens: Enhances existing infrastructure and creates new modes of interaction.

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