Autonomous Agents in Travel: What's Working Today
A practical look at where AI agents are already useful in travel booking, hotel negotiation, and itinerary planning—and where humans still need to confirm the final decision.
Windrose AI is about how the agentic web actually works.
Not the demos. Not the buzzwords.
The real systems - protocols, APIs, trust layers, and the messy constraints that show up in production when software is used by agents instead of humans.
If you're building or reasoning about these systems, this is where the details matter.
A practical look at where AI agents are already useful in travel booking, hotel negotiation, and itinerary planning—and where humans still need to confirm the final decision.
A practical guide to the difference between chatbots and agents: agency, tool use, goal persistence, and autonomous execution—and where today’s AI products actually sit on that spectrum.
A practical comparison of Claude and GPT-4o as agentic runtimes, focusing on tool use, long-horizon tasks, recovery from errors, and instruction following across realistic benchmark-style workflows.
Designing for AI agents means making APIs explicit, predictable, and safe to automate. The key ingredients are structured errors, capability declarations, idempotency, quote-before-commit flows, and unambiguous schemas.
AI agents need identities that are portable, scoped, and revocable. This post compares API keys, wallet-based identity, and OAuth-style delegation, and argues that the right model is a layered one.
Agents do not just change how people buy; they change what gets priced, how bundles are assembled, how often purchases happen, and how loyalty is earned when software optimizes for cost and availability instead of habit.
A practical guide to how AI agents find, understand, and use services today—from .well-known endpoints and llms.txt to OpenAPI specs and store manifests.
A practical guide for developers on making an online store usable by AI agents: discovery endpoints, machine-readable product data, agent authentication, and x402 payments.
A practical framework for deciding when an autonomous agent should stop and ask for confirmation, based on reversibility, spend, ambiguity, and novelty.
What llms.txt is, why it matters for agent-native websites, and how to write one that helps AI systems find the right content without guessing.