✈ TravelAgent Demo

Evaluation of the Muse AI agent — we used Muse to evaluate Muse.

A demo of Meta's Muse model doing real travel work: planning a Kyoto + Tokyo/Hakone family trip.

This demo was created from this text:

✎ ORIGINAL PROMPT

We work in travel marketing B2B2C niche, using mostly agent portals (for now) to bridge supplier content to agents and consumers. Currently mostly legacy code and websites. This project is a quick deep dive to understand the differences and intersections between:

  • Seasoned travel agent — decades of experience, deep industry and destination knowledge: tips, tricks, things to see and things to avoid. People who actually use great travel agents rave about them afterward — pitfalls avoided, better experience, often saving money.
  • Beginner travel agent — someone new, or under a decade in the industry. Where is the gap between their business and a seasoned veteran's? How can we, in the B2B2C niche, bring more value to the average agent — taking them from beginner to offering customers the ultimate curated travel plans? That means identifying exactly what value a travel agent provides, why a customer should prefer one, the best marketing strategies, and the best ways to add value for travel suppliers so their products get visible and highlighted to agents and agent customers.
  • LLM-driven "Travel Agent" — the models and their harness intelligence curve are pointing up, so we ran a direct test of what an LLM agent can do today: a quality travel-agent experience delivered from an LLM. Once complete, we compare that system's strengths and weaknesses against a quality experience with a human travel agent.
  • Extrapolation — from this data, the strengths of in-person contact versus artificial intelligence: reasons plus potential tools we can offer agents and suppliers to create a strong connection to their customers — trust, friendship, and knowledge for their next adventures.

Outputs: markdown for each of the areas above — clear, concise, easy for non-technical people to read. Solutions, not problems: what is available today, and anticipate tomorrow, so the solutions we build today are not obsolete in a year or a month. Plus a very simple LLM-based virtual travel agent demo — a Python app with an LLM call, a clean professional UI showing a travel portal an agent might use (prefilled with our Kyoto + Tokyo/Hakone test trip), trip results in a clean scannable format with follow-up questions and share/book actions. And as a summary, an HTML page of what we learned: B2B2C solutions for today, tomorrow, and the foreseeable future.