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Job-Ready Portfolio Project: Build a Full-Stack Agent That Books Travel Using Qwen

UUnknown

2026-02-23

9 min read

Build a job-ready portfolio project: a full-stack travel agent using Qwen for agentic orchestration—includes repo checklist, demo script, and interview tips.

Ship a Job-Ready Portfolio Project: Full-Stack Travel Agent Using Qwen

Hook: You're competing for senior dev roles and need a single portfolio project that demonstrates system design, API integration, agentic AI, and production-ready practices. Build a full-stack travel booking agent that searches, books, and confirms trips using Qwen for agent logic—complete with a deployable app, tests, and a demo script recruiters can run in 5 minutes.

Why this project matters in 2026

Through late 2025 and into 2026, agentic AI—models that take multi-step, real-world actions—moved from research demos to production integrations. Alibaba's upgrades to Qwen added agentic capabilities for tasks like ordering food and booking travel (Jan 2025–2026 announcements). Employers now expect practical experience composing LLM-based agents that interact with APIs, manage state, and handle failure modes. This project hits the intersection of product thinking and engineering rigor: it shows you can design an agent, integrate third-party services, and deliver a reliable product.

Project overview: goals and scope

Deliver a full-stack application that:

Allows users to search flights and hotels, compare options, and place bookings.
Uses Qwen for agent orchestration: plan search queries, call APIs, confirm details, and generate user-facing confirmations.
Includes a secure backend, database persistence, automated tests, CI/CD, and a one-click demo deploy.

Minimum Viable Features (MVP)

Search flights and hotels (mock or real provider API)
Book a single itinerary with payment simulation
Agent-driven confirmation email and timeline generation
Admin endpoint to view bookings and logs

High-level architecture

Keep the architecture simple but demonstrative of production patterns:

Frontend: React (or Svelte/Vue), TypeScript, Tailwind for quick UI, authentication via JWT/OAuth (demo using email link).
Backend: Node.js + Express or Fastify, TypeScript, REST + Webhooks.
Agent Layer: Qwen calls orchestrate business flows. Implement a domain-specific planner that issues backend API calls and handles retries.
Third-party APIs: Travel aggregator or mocked provider (Amadeus/Skyscanner pattern). Use a mock provider in the repo with seeded data to avoid credentials in demos.
Data: PostgreSQL or SQLite for portability; store bookings, users, and agent conversation logs.
Deployment: Docker, GitHub Actions for CI/CD, and deploy to Vercel/Render/AWS for the demo.

Designing the agent flow

Break the agent into clear responsibilities. The Qwen agent should not directly call provider APIs; instead, it should emit structured actions that your backend executes. This separation improves safety, observability, and repeatability.

Recommended action schema

{
  "action": "search_flights",
  "params": {
    "from": "SFO",
    "to": "JFK",
    "depart_date": "2026-04-15",
    "return_date": "2026-04-20",
    "passengers": 1
  }
}

Qwen returns a sequence of actions like search_flights, choose_option, book_itinerary, and send_confirmation. Your backend validates and executes.

Sample agent exchange (conceptual)

// Frontend submits user intent
POST /api/agent/plan
{ "intent": "book a round-trip from SFO to JFK on Apr 15 returning Apr 20" }

// Backend calls Qwen with conversation + schema instructions
// Qwen returns a plan of actions. Backend executes each action and streams results back to client.

Integrating Qwen (practical tips)

As of 2026, Qwen's agentic features support multi-turn planning and tool usage patterns. Keep the integration robust:

Instruction tuning: Provide Qwen with a short system prompt describing available actions and constraints (e.g., budget limits, max retries).
Validated output: Always parse Qwen outputs against a JSON schema and reject unsafe or malformed actions.
Tool isolation: Implement each tool as an idempotent backend endpoint (search, pricing, hold, book, cancel).
Rate limits & backoffs: Wrap provider calls with exponential backoff and circuit breakers.

Example: call Qwen from Node

// Pseudocode demonstrating pattern; replace endpoint & auth as per Qwen SDK
const fetch = require('node-fetch');

async function callQwen(prompt, conversation) {
  const resp = await fetch('https://api.qwen.example/v1/agent', {
    method: 'POST',
    headers: { 'Authorization': `Bearer ${process.env.QWEN_API_KEY}`, 'Content-Type': 'application/json' },
    body: JSON.stringify({ prompt, conversation })
  });
  const json = await resp.json();
  return json; // parse and validate
}

Replace with Qwen's official SDK or REST endpoint in your repo and document version and scope. In the repo, keep a .env.example and default to using the mock provider for demos.

Backend patterns and safety

Make the backend authoritative. The agent suggests actions; your backend enforces business rules and validates steps.

Idempotency keys: Use idempotency for booking endpoints to avoid double-charges.
Audit trail: Log agent input/output and each executed action for debugging and interviews.
Mock vs. provider: Abstract provider calls behind an interface so you can switch from mocked data to a real aggregator with minimal changes.

Data model (simplified)

// PostgreSQL tables (simplified)
users(id, email, name, created_at)
bookings(id, user_id, status, total_price_cents, provider_reference, created_at)
itineraries(id, booking_id, type, origin, destination, depart_at, arrive_at, carrier, fare_class)
agent_logs(id, booking_id, input, qwen_output, executed_actions, timestamp)

Testing and CI/CD

Showcase testing depth—employ unit, integration, and end-to-end tests:

Unit tests: Agent output parsing, action schema validation.
Integration tests: Backend endpoints with mocked provider responses.
E2E tests: Playwright/Cypress to book an itinerary using the mock provider.
Contract tests: Ensure the action schema contract between Qwen and your backend doesn't break.

Set up GitHub Actions to run tests, lint, and build containers. Include an automatic deploy job to your demo environment on merges to main.

Repository checklist: What to include (copy into README)

Project summary with a short demo GIF and quick setup (5-minute local run).
Architecture diagram and explanation of the agent pattern.
Environment variables and .env.example with mock defaults.
Sample requests and Postman/Insomnia collection.
Automated tests and an easy-to-run local test command.
Dockerfile and docker-compose for local dev using SQLite or Postgres container.
CI workflow file and deploy configuration for a free-tier host.
Security notes: how secrets are stored and how to rotate API keys.
Demo script for interviewers—5 steps to recreate core flows.
Changelog and roadmap of advanced features.

Demo tips: run a crisp 5-minute interview presentation

Recruiters and hiring managers value clarity. Prepare a demo script and pre-seed your demo environment to avoid waiting on network calls.

Start with intent: “I’ll show the user flow and the agent orchestration.”
Show architecture briefly: highlight where Qwen lives and what it controls.
Run a live booking: Use seeded accounts and a pre-funded test payment to avoid failures.
Open agent logs: Walk through the Qwen plan (actions) and the backend execution trace.
Fail fast demo: Force a provider error (simulate timeout) and show how the agent and backend recover.
Metrics & observability: Show the dashboard with request latencies and agent success rate.

Advanced features to highlight (nice-to-have)

Multi-modal input: Accept screenshots or PDFs (itinerary PDFs) and have Qwen extract booking data (2026 LLMs are strong at this).
Cost heuristics: Agent enforces budget constraints and suggests cheaper alternatives.
Human-in-the-loop: Add manual approval for bookings above a threshold—you can demo escalation flows.
Personalization: Keep traveler preferences in the profile and ask Qwen to prioritize them.

Interview talking points and career framing

When discussing this project, emphasize:

Design decisions: why the agent emits actions rather than executing them directly.
Safety & validation: schemas, idempotency, and audit logs.
Trade-offs: mocked provider vs. production aggregator, and why you chose portability for recruiters.
Performance: caching search results and user experience improvements (optimistic UI, streaming results).

Observability & metrics to show in interviews

Prepare a small dashboard (Grafana or lightweight charts) with:

Agent action success rate
Average time to complete a booking
Number of retried provider calls
Test coverage percentage

Security & privacy notes

Travel data is sensitive. For a portfolio project:

Use fake PII or clearly label seeded demo accounts.
Never commit real API keys—use GitHub Secrets for CI and .env for local dev.
Encrypt tokens at rest and enforce TLS for all endpoints.

2026 trends and future-proofing your project

In 2026, expect these trends to be interview talking points you can leverage:

Agent orchestration frameworks: More mature SDKs (Qwen and others) will add policy layers and integrated tool registries—structure your agent to swap in new SDKs cleanly.
Local-first LLMs: Desktop/local agent previews (like Anthropic’s Cowork trend) mean employers will value models that can run offline or hybrid—document how you'd add a local agent option.
Regulation & compliance: Data handling constraints are tightening; show awareness of GDPR/CCPA-style controls in your README.
Agent evaluation: Provide test harnesses that automate safety and correctness checks for agent plans (important for production roles).

Tip: Recruiters love reproducibility. A one-click demo (Vercel preview link + seeded DB) is worth more than a fancy UI.

Project timeline — 2-week plan (practical roadmap)

Days 1–2: Scaffold frontend and backend, wire basic routes and UI mocks.
Days 3–5: Implement the mock provider, database schema, and agent action schema.
Days 6–9: Integrate Qwen for plan generation; add parsing and validation layers.
Days 10–11: Add tests, CI, and Docker; write README and demo GIFs.
Days 12–14: Polish, deploy demo, create interview script and video walkthrough.

README skeleton (what to include at the top of the repo)

Project one-liner and demo GIF.
Quickstart (5-minute local run).
Architecture diagram and agent action schema.
How to run tests and deploy preview.
Interview/demo script and suggested talking points.

Example README Quickstart (copyable)

git clone https://github.com/yourname/qwen-travel-agent
cd qwen-travel-agent
cp .env.example .env
# For demo, use mocked provider
docker-compose up --build
# Open http://localhost:3000 and log in with demo@demo.com

Measuring success for hiring managers

When interviewers evaluate this project, they'll look for:

Clean separation of agent logic and executors
Production patterns: idempotency, retries, CI/CD, and tests
Observability and reproducible demos
Ability to explain trade-offs and future enhancements

Wrap-up: Deliverables to include in your portfolio

Live demo link + GIFs
Repo with README, diagram, and quickstart
Short video walkthrough (3–5 minutes)
Interview script with 5 discussion points
Key metrics dashboard screenshot

Final thoughts and next steps

Building a Qwen-powered travel booking agent is more than a coding exercise—it's a compact case study demonstrating system design, safe agent orchestration, integration patterns, and product thinking. By keeping the agent as a planner that emits verifiable actions, you combine the creative strengths of LLMs with the control and safety required in production systems.

Start small with mocked providers and a seeded demo, then extend to real aggregator APIs for production work. In interviews, focus on the choices you made and how they'd scale to real-world constraints. In 2026, teams hiring for AI-forward roles want practical, demonstrable experience—this project gives you exactly that.

Call to action

Ready to build? Fork the provided template, follow the README quickstart, and post your demo link on LinkedIn with a short case study describing your architecture and trade-offs. Need a review before your interview? Share your repo and demo link—I'll audit it for clarity, safety, and impact.

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