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.
AI agents are starting to buy from and sell to other AI agents. This post examines what an autonomous transaction actually requires, which protocols exist today, and where the system still breaks.
A practical map of the agentic web stack, from model inference to tools, transport, auth, payment, and fulfillment—what each layer does and which protocols or projects own it.
Amazon’s 1-Click patent reduced checkout friction. Agents go further by combining pre-authorized spending, saved preferences, and structured product APIs to remove most of the steps between intent and purchase.
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.