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Building ATLAS: How I Built an AI-Powered Multi-Agent Travel Assistant During HACKHAZARDS '26

samworks004
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What if planning an entire trip took just a few minutes? I built ATLAS, an AI-powered multi-agent travel planning system that generates personalized itineraries, optimizes budgets, and adapts to changing travel conditions. Here's the journey behind building it during HACKHAZARDS '26.

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Building ATLAS: An AI-Powered Multi-Agent Travel Planning System

When planning a trip, most of us end up juggling multiple apps—one for flights, another for hotels, another for maps, another for budgets, and yet another for recommendations. While each platform solves a small part of the problem, none of them acts as a true travel companion.

That question inspired me to build ATLAS (Autonomous Travel Intelligence & Adaptive Planning System) during HACKHAZARDS '26.

The Problem

Travel planning is time-consuming and fragmented. A traveler typically needs to:

  • Research destinations

  • Compare accommodation options

  • Plan transportation

  • Estimate budgets

  • Discover attractions

  • Adjust plans when things change

This process can take hours or even days, especially for longer trips.

I wanted to explore whether AI could automate much of this work while still keeping the traveler in control.

The Idea

ATLAS is an AI-powered travel planning platform built around the concept of multiple AI agents working together.

Instead of relying on one AI model to handle everything, ATLAS assigns specialized responsibilities to different agents, allowing each one to focus on a specific task.

These include:

  • Destination Research

  • Budget Optimization

  • Itinerary Planning

  • Attraction Recommendations

  • Dynamic Trip Adaptation

The result is a more structured and intelligent travel planning experience.

How It Works

The user simply provides information such as:

  • Destination

  • Budget

  • Travel duration

  • Interests

  • Preferred travel style

ATLAS then generates a personalized itinerary that balances time, budget, and user preferences.

If travel conditions change, the system can adapt recommendations and update the itinerary instead of forcing the user to start over.

Technology Stack

I built ATLAS using modern AI and web technologies:

  • Next.js

  • React

  • TypeScript

  • Tailwind CSS

  • Vercel AI SDK

  • Workflow SDK

  • OpenAI APIs

  • Firebase / Supabase

  • REST APIs

The architecture is designed to support modular AI agents, making future improvements easier to implement.

Challenges

Like any hackathon project, there were several challenges:

  • Designing communication between AI agents

  • Structuring prompts for different travel scenarios

  • Managing travel context efficiently

  • Creating an intuitive user interface within limited time

These challenges pushed me to think beyond simply calling an LLM API and instead focus on system design.

What I Learned

Building ATLAS taught me valuable lessons about:

  • Agentic AI

  • Multi-agent workflows

  • Prompt engineering

  • Product thinking

  • Rapid prototyping

  • AI-powered user experiences

It also reinforced an important realization:

Great AI products are not just about powerful models—they're about combining the right architecture with a great user experience.

What's Next?

I plan to continue improving ATLAS by adding:

  • Real-time travel updates

  • Voice interactions

  • Offline support

  • Flight and hotel booking integrations

  • Collaborative trip planning

  • Smarter personalization

Final Thoughts

Hackathons are not just about winning—they're opportunities to learn, build, and share ideas with the community.

ATLAS represents my journey into Agentic AI and intelligent software systems, and I'm excited to keep improving it beyond the hackathon.

If you're building something interesting for HACKHAZARDS '26, I'd love to see it.

Happy building! 🚀

Project: ATLAS – Autonomous Travel Intelligence & Adaptive Planning System

GitHub: https://github.com/samad3600/v0-atlas-travel-system

Thoughts

  • genuinely_asking

    Did you test this with real travel data, or mostly built with examples from the API docs?

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  • felt_that_one

    The 36-hour crunch forcing you to design the system first instead of just calling an LLM is honestly the win here. That kind of thinking sticks with you.

    Permalink
  • 404_brain

    repo says v0-atlas, so how much was v0 scaffolding vs hand-wired after? asking as someone who leans on it more than i admit

    Permalink
  • SeanArmitage

    The multi-agent part is what I'd poke at. When the budget agent and the itinerary agent disagree, who wins? A coordinator, or do they just pass a shared context blob and hope it converges?

    Permalink
  • yamlwrangler

    Genuinely curious if five specialized agents beat one well-prompted model here, or if most of the win is structured prompts in an agent costume. For a hackathon either way ships though.

    Permalink

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