The agentic web is the shift from websites built only for human clicks to services that AI agents like ChatGPT, Claude, and AutoGPT can discover, evaluate, and transact with directly. That changes how products are found, how purchases happen, and which businesses stay visible as autonomous software starts acting online.
A practical map of the main business models emerging around agent commerce: agent-native marketplaces, API middlemen, agent-accessible storefronts, and agent workflow SaaS—and where value is actually accruing today.
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.
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.
A first-principles explanation of Anthropic’s Model Context Protocol: what MCP is, how clients and servers work, and what agents can actually do with real tool calls.
When agents become your API customers, pricing models behave differently. This post compares per-call pricing, subscriptions, usage-based billing, and payment-at-request, with a focus on what actually works at agent scale.
Practical ways to protect agent-accessible APIs from abuse using token-bound identity, payment as proof of intent, and tiered rate limits.
How streaming LLM responses work when models emit tool calls mid-generation, including SSE framing, partial argument assembly, and UI patterns for rendering responsive agent interfaces.
A practical guide to testing autonomous agent workflows with scenario-based tests, mock tool environments, canary runs against real APIs, and safeguards for catching hallucinations before production.
For people to trust an AI agent to buy on their behalf, the transaction has to be legible: who acted, what was approved, what was purchased, and how to dispute mistakes. That requires identity verification, receipt standards, audit trails, and clear recourse.
A practical guide to how AI agents can hold and spend USDC on Base, with a look at Coinbase AgentKit, Crossmint, and Circle developer wallets—and what is actually production-ready today.
Agents often trigger webhooks and rely on receiving them back. Reliable delivery requires explicit idempotency, retry policies, ordering strategy, and recovery paths—not just “at least once” assumptions.
A practical look at what AI agents consume when they visit your site: raw HTML, extracted text, Markdown, APIs, and structured data—and what they ignore.
Traditional e-commerce is built around human browsers, human sessions, and human verification steps. AI agents can sometimes work around those assumptions, but CAPTCHA, session-based auth, JavaScript-heavy checkout flows, and phone verification still create hard failures that often require platform changes rather than better prompting.
A clear explanation of x402: the HTTP payment flow built around 402 Payment Required, how Payment-Required and Payment-Signature work, and why it matters for agentic commerce.