Plan a 5-day trip to Japan. Tokyo + Kyoto. $3,000 budget. Love food and temples, hate crowds. That single sentence is the input. The output is a validated, day-by-day itinerary with real POIs, neighborhood-level stays, transport legs between cities, a budget breakdown that adds up, and a Critic that says passed or sends specific agents back to revise. Most "AI travel planner" demos are a single mega-prompt that hallucinates fluently. I wanted to find out what changes when you treat it as a systems problem instead of a prompting problem — typed contracts, specialized agents, deterministic validation, retries, and observability. This post walks through the architecture, the agents, the Critic loop, the schemas, and the production concerns (cost, tracing, edge cases). All code is Python, all LLM calls go through Gemini Flash on the free tier (~$0 per run), and the same LLMClient interface swaps to Claude or OpenAI in one file.…