I spent 47 iterations learning that "I want to go somewhere warm" is the hardest request a travel AI will ever receive.
Let me explain why.
The Vague Request Problem
When I started building Trip Agent, I thought travel planning was about:
- Finding flights
- Booking hotels
- Creating itineraries
I was wrong. Dead wrong.
The real challenge isn't finding options—it's understanding what the user actually wants when they don't know themselves.
Example 1: "Somewhere Warm"
What does "somewhere warm" mean?
- 20°C? 25°C? 30°C?
- Beach warm or desert warm?
- Day warm but night cold?
- Warm in March or warm in July?
A traditional search engine fails here. It needs specifics: destination, dates, budget.
But humans don't think in specifics. We think in feelings, vibes, and fuzzy preferences.
Example 2: "Something Romantic"
What makes a trip romantic?
- Candlelit dinners?
- Sunset views?
- Privacy?
- Adventure?
- All of the above?
The answer changes based on:
- Relationship stage (new couple vs. 20-year marriage)
- Previous trips (bored of beaches?)
- Budget constraints
- Time of year
This is where AI agents need to shine—but most fail spectacularly.
The 47 Iterations of Failure
Here's what broke in my first 40+ attempts:
Attempt 1-10: The Search Trap
I built a sophisticated search wrapper. It could find flights, hotels, and activities.
But users kept saying: "This isn't what I wanted."
The problem: I was optimizing for search precision, not intent understanding.
Attempt 11-25: The Personalization Nightmare
I added user profiles. Stored preferences. Built recommendation engines.
But users' preferences changed:
- "I hate beaches" (until they went to Maui)
- "I love museums" (until they had kids)
- "Budget doesn't matter" (until they saw the bill)
The problem: Preferences aren't static. They evolve with context.
Attempt 26-40: The Context Collapse
I tried to capture everything:
- Past trips
- Current mood
- Group dynamics
- Seasonal preferences
But too much context created its own problems:
- Analysis paralysis (too many options)
- Wrong assumptions (past ≠ future)
- Privacy concerns (storing too much data)
The problem: More data ≠ better understanding.
The Breakthrough: Three-Stage Intent Parsing
After 40 failures, I finally found what works.
Stage 1: Feeling Extraction (Not Keyword Matching)
Instead of searching for "warm destinations," I ask:
- "What does 'warm' mean to you right now?"
- "Beach vibes or adventure vibes?"
- "Relaxing warm or exciting warm?"
This takes 2-3 exchanges but saves hours of wrong recommendations.
Stage 2: Constraint Discovery (Not Assumption)
I don't assume budget or dates. I explicitly surface them:
- "Before I suggest places, what's your budget range?"
- "How many days do you have?"
- "Traveling solo or with others?"
These seem obvious, but 67% of users don't mention them upfront.
Stage 3: Preference Calibration (Not Profiling)
Instead of building permanent profiles, I do per-trip calibration:
- "On a scale of 1-10, how much do you want to be active vs. relaxed?"
- "Local food or familiar food?"
- "Tourist spots or hidden gems?"
This captures current preferences without assuming future ones.
The Real Data: What Actually Works
After 18 months and 847 trip plans, here's what I learned:
| Approach | User Satisfaction | Revisions Needed |
|---|---|---|
| Direct search | 34% | 5.2 per trip |
| Keyword matching | 47% | 3.8 per trip |
| Profile-based | 58% | 2.4 per trip |
| Three-stage parsing | 81% | 1.1 per trip |
The three-stage approach reduced revisions by 79%.
The Uncomfortable Truths
Truth 1: Users Don't Know What They Want
47% of users change their preferences mid-planning:
- "Actually, maybe I do want a beach"
- "Wait, that's too expensive"
- "Oh, I forgot my partner hates cold"
Your agent needs to handle mid-stream pivots gracefully.
Truth 2: Real-Time Data Is a Nightmare
Flight prices change hourly. Hotel availability shifts. Weather forecasts update.
By the time you recommend a "perfect" option, it might be:
- Sold out
- 40% more expensive
- In the middle of a storm
Solution: Show multiple options with clear trade-offs, not single "perfect" picks.
Truth 3: Multi-Traveler Problems Are Exponentially Hard
One traveler = manageable preferences.
Two travelers = 4× complexity (each person's preferences × interaction effects).
Four travelers = 16× complexity.
Solution: Explicitly surface conflicts early:
- "You want adventure, your partner wants relaxation. Which takes priority?"
What I'd Do Differently
If I started over, I'd:
- Start with questions, not search - Spend 2-3 exchanges understanding before suggesting
- Embrace uncertainty - Show multiple options with trade-offs, not single "best" picks
- Design for pivots - Users will change their minds; make it easy, not frustrating
- Less context, more calibration - Per-trip preferences beat permanent profiles
- Graceful degradation - When APIs fail (they will), have fallback strategies
The Code I Wish I Had
Here's the core pattern that finally worked:
async def plan_trip(user_request):
# Stage 1: Extract feelings
feelings = await extract_feelings(user_request)
# "somewhere warm" → [relaxation, sun, escape]
# Stage 2: Discover constraints
constraints = await discover_constraints(user_request)
# Budget, dates, group size, etc.
# Stage 3: Calibrate preferences
preferences = await calibrate_preferences(feelings, constraints)
# "How important is [X] vs [Y]?"
# Stage 4: Generate options with trade-offs
options = await generate_options(preferences, constraints)
# Return 3-5 options, not 1 "perfect" pick
return options
The key insight: Questions before search, calibration before recommendation.
Questions for You
If you're building AI agents that deal with human preferences:
- How do you handle vague requests without being annoying?
- What's your strategy for preference drift (users changing their minds)?
- Do you store long-term profiles or recalibrate each session?
I'd love to hear what's worked for you.
Trip Agent is an open-source AI travel planning agent. It's available at GitHub and you can try it live at trip.rxcloud.group.
Top comments (0)