AI as Your New Client: Why Products Must Be Built for AI Agents
Author
Андрій Орлов
Founder of Ople Agency
Topic
Future products will be built not only for people but also for AI agents. Let's explore how the approach to development and marketing will change.
How is product selection done today?
Today, a person searches Google for a service for a specific task: where to keep a client database, what to replace manual reports with, or how to quickly connect online payments. Then they open the website, look at the subscription price, look for possible limitations, watch a short demo of the product, and based on this, make a decision whether it is worth paying.
How will an AI agent do it?
An agent will act completely differently. It does not need a beautiful first screen of a landing page with a bright promise to "accelerate your business by 3 times". It ignores emotional triggers and marketing traps.
Instead, the agent will immediately go to read the technical documentation, pricing page, security policy, API specifications, availability of MCP servers, and objective comparison with competitors. Only after a deep analysis of this data will it decide whether this service can be given to its owner — a human.
“If the essence of your product cannot be understood from the documentation and connection examples, the AI agent will quickly hit a wall and simply reject your service.
Key changes in the approach
The transition from B2B to B2A (Business-to-Agent) requires a rethinking of every point of contact with the user.
Beautiful design is important to a human, but an agent analyzes only structured data. Emotional triggers don't work on machines.
They become a normal way to give the agent access to the product. The agent must understand in a machine-readable way what actions it can trigger.
No hidden conditions. The machine must accurately calculate the unit economics of using your service without surprises.
What access does the service ask for? Where is data stored? How quickly can a project be deleted? These are critical trust markers for the algorithm.
Documentation must not only be understandable to humans but also easily parsable by agents. OpenAPI specs and clear schemas are critical.
If an API response takes too long or frequently fails, the agent will automatically reject the service as unreliable during initial testing.
The agent won't read filtered reviews on your landing page. Instead, it will analyze open GitHub bugs, StackOverflow mentions, and real historical uptime.
What next?
It is worth noting that specialized services for AI agents are already being actively implemented: systems of long-term memory between different tools, autonomous payments, data access protocols, action verification mechanisms, etc.
For developers, startups, and companies, this is an extremely important signal. When you create a new product or a pet project, think not only about the living person on the site. Think about Codex, Claude Code, or any other autonomous agent that must quickly understand: what specific task your service covers and how to technically connect to it.
For now, the concept of "AI agent as a buyer" sounds a bit unusual. But it is worth remembering that just a few years ago, the idea of "I will write a task in plain text, and the neural network will write the first version of my project itself" also seemed like pure science fiction. The new era of B2A is already near, and we should prepare for it today.