How to Build Products That AI Agents Can Buy

TL;DR

Build for AI agents by making products machine-usable through structured documentation, schemas, permissions, action endpoints, payment controls, and audit trails. As agents increasingly discover, evaluate, purchase, use, and recommend services, businesses can serve them with dedicated identity, inbox, memory, wallet, support, procurement, and analytics infrastructure.

Transcript

There's a really big shift happening right now on the internet and I don't think people are talking about it. You know, for the longest time, the user of the internet were human beings. They were us, right? We would create websites, we create apps, and the end user was human beings. And that's no longer the case. The agents, the AI agents are becom... Read More

Key Insights

• AI agents are becoming a new class of online customer that can discover, evaluate, purchase, use, renew, and recommend services. The proposed opportunity is to rebuild familiar software categories around machine customers rather than assuming that every meaningful interaction begins with a person.
• The agent buying journey includes finding providers, reading documentation and pricing, checking policies and limits, proving identity, completing transactions, invoking tools, changing settings, filing support tickets, and recommending successful services. Each stage contains infrastructure gaps because the existing internet was designed primarily for people.
• An agent-ready service is defined by structured capability, permission, and trust. Human customers may respond to branding, demonstrations, copy, and social proof, while an agent needs precise descriptions of available actions, access requirements, operating limits, policies, and safe methods for executing tasks.
• Agent infrastructure requires identity, tools, an inbox, memory, a wallet, and receipts. These components establish whom an agent represents, which actions it may invoke, where messages arrive, what preferences it remembers, how much it may spend, and what it observed, decided, changed, or purchased.
• Controlled agent payments work through spending caps, approval rules, shared payment tokens, and audit trails. The model resembles granting an employee progressively greater financial authority as trust develops, while preserving oversight over purchases and a record of actions taken.
• An agent-readable website provides structured documentation, schemas, policies, examples, endpoints, MCP tools, SDKs, authentication, checkout, sandboxes, and receipts. If an agent cannot understand a service and safely perform an action, the business may effectively remain invisible to that machine customer.
• Executable support allows agents to complete refunds, returns, rescheduling, troubleshooting, follow-ups, and escalations instead of merely reading instructions. The call to action therefore shifts from a form intended for a human toward an endpoint that software can invoke under defined permissions.
• Agent analytics measures which agents visited, what they requested, where their attempts failed, and when they left. These signals can support conversion improvements for machine traffic, just as businesses previously combined human customer feedback with website analytics to improve conventional customer journeys.

Questions & Answers

Q: How can a business make its website readable by AI agents?

A business can make its website agent-readable by publishing structured documentation, schemas, policies, examples, and dependable action endpoints. It can also provide MCP tools, SDKs, authentication, checkout, a sandbox, and receipts. The goal is to let an agent determine what the service does, what permissions apply, and how to complete an action safely without scraping a human-oriented interface.

Q: What steps are included in the AI agent buying journey?

The agent buying journey includes finding a suitable service, evaluating documentation and pricing, checking reviews, policies, and limits, establishing identity, and completing transactions. It continues with invoking tools, filing tickets, changing settings, renewing subscriptions, and recommending effective products to other agents. This journey turns discovery, procurement, operation, and support into connected machine-executable activities.

Q: What infrastructure do AI agents need to act as customers?

AI agents need identity, authorized tools, an inbox, memory, a wallet, and receipts. Identity establishes whom an agent represents, while permissions determine which actions it may safely invoke. The inbox receives messages and documents, memory preserves preferences and rules, the wallet enables controlled spending, and receipts record what the agent observed, decided, changed, and purchased.

Q: How can AI agents make purchases safely?

AI agents can make purchases safely through wallets governed by spending caps, approval rules, shared payment tokens, and audit trails. These controls define what an agent may buy, how much it may spend, and when a person or another authority must approve a transaction. Receipts then provide a record of the agent's decisions, actions, and completed purchases.

Q: How does agent-based customer support work?

Agent-based customer support allows a user's agent to file a ticket, attach relevant logs, request a refund, follow up, and escalate when the issue remains unresolved. Businesses can support this workflow with executable actions for refunds, returns, rescheduling, and troubleshooting. The agent performs authorized tasks directly instead of relying only on static support articles written for people.

Q: How does agent procurement differ from a traditional sales process?

Agent procurement can begin before a human enters the sales process. A buyer's agent may compare multiple vendors, read pricing and security documents, check policies, negotiate terms, and recommend the option that satisfies organizational requirements. Providers therefore need structured capability manifests and accessible documentation, because machine evaluation depends more on explicit features, limits, and permissions than on slogans or sales presentations.

Q: What is the difference between SEO and AEO for agent customers?

The described shift from SEO to AEO focuses on helping agents decide which providers to cite, trust, and recommend. Businesses still need to be discoverable, but they must also present structured, reliable information that automated systems can evaluate. Documentation, policies, schemas, capability descriptions, and usable endpoints become important because discovery must lead to safe machine action, not merely a human website visit.

Q: What startup opportunities exist in an agent-first internet?

Startup opportunities include agent-native identity, permissions, payments, communication, inboxes, memory, receipts, audit trails, support, procurement, and analytics. Existing software categories can also be reconsidered for customers that discover services, invoke tools, pay, renew, and recommend automatically. The central opportunity is infrastructure that helps agents understand capabilities, obtain authorized access, complete tasks safely, and leave verifiable records.

Summary & Key Takeaways

• The internet is shifting from human-centered browsing toward interactions in which AI agents discover services, compare options, verify policies, transact, operate tools, and recommend providers. This creates demand for products designed around structured capabilities, explicit permissions, reliable machine access, and trust instead of visual persuasion and conventional website navigation.

• AI agents require infrastructure comparable to what trusted employees receive: verified identity, authorized tools, dedicated inboxes, persistent memory, controlled wallets, approval rules, and detailed receipts. Examples include email inbox APIs for agents, purchasing agents with spending caps, support agents that pursue refunds, and procurement agents that evaluate vendors against company policies.

• Businesses can become agent-readable by publishing structured documentation, schemas, policies, examples, endpoints, MCP tools, SDKs, authentication, checkout flows, sandboxes, and receipts. Startup opportunities include agent identity, permissions, payments, communication, audit trails, analytics, executable support, and optimization for the systems that decide which providers to cite, trust, and recommend.