Main User is AI. Why API-first is the New Business Reality

9 Feb, 2026
| Salome K

THE END OF THE HUMAN ERA: WHY YOUR MAIN USER IS NO LONGER A PERSON

When the creator of OpenClaw at Y Combinator stated that routine tasks would soon be handled exclusively by AI agents, many dismissed it as yet another techno-utopian fantasy. But those who heard a call to immediate action are already rewriting their product roadmaps today. Because his next thesis is not a forecast—it is a verdict on the old paradigm: the primary users of web services will no longer be humans, but AI agents.

This is not an interface shift. It is a fundamental breakdown of the entire logic of digital business. If your service is designed for regular, routine operations—from generating reports and booking hotels to price monitoring and ad campaign management—your target audience now splits into two segments.
The first, rapidly shrinking, consists of humans who still do these tasks manually out of habit or distrust.
The second, growing exponentially, consists of autonomous AI agents that will perform these tasks on their behalf. And these agents will not choose services based on UI aesthetics, but on one key criterion: machine usability.

CURRENT REALITY: A WORLD WHERE THE BROWSER IS THE NEW COBOL

Today, most web services are “digital façades” designed for human perception: beautiful buttons, complex animations, intuitive navigation. For an AI agent, this façade is a complex, illogical, and extremely inefficient barrier.

Imagine if every time you wanted to check your account balance, you had to walk into a beautifully designed bank building, visually search for the right counter, smile at a clerk, and only then receive a number. That is exactly what parsing a typical web page looks like to an AI.

What is happening right now:

Explosive growth of agent frameworks: AutoGPT, LangChain, CrewAI. These tools enable the creation of autonomous agents capable of setting goals and executing complex multi-step tasks in digital environments. Their weak point is fragile interaction with unpredictable, human-designed interfaces.
A quiet rebellion of integration developers: Companies built on automation (Zapier, Make, n8n) are already facing the reality that maintaining thousands of “scraping connectors” to popular services is a never-ending whack-a-mole game. One UI redesign—and the entire automation chain collapses. Their most frequent and costly request to SaaS platforms is simple: “Give us a stable API.”
The emergence of a hierarchy of “smart” users: Today there are already two types of customers. One logs into Trello to manually move a card. Another—a semi-agent (a human with a browser extension or ChatGPT)—tries to make AI do it through the UI, running into errors and misunderstandings. A future fully autonomous agent will look for a direct communication channel with the service core—the API. Whoever provides the best access will win.

The conclusion is obvious: a service without a first-class, complete, well-documented API is a digital dead end. It is like a store that refuses to accept credit cards, insisting on cash only. Yes, some customers will remain—but the largest, most valuable, and fastest-growing segment will pass it by.

THE TREND: API-FIRST AS THE NEW DEVELOPMENT RELIGION AND THE “HUMAN / AI” SWITCH

This is where the central trend of the new era is born—Machine-First Development. It can be broken down into two dogmas.

Dogma One: API-First Is Not an Option—It Is the Only Path

The principle “backend with an API first, frontend later” is no longer just good engineering practice. It is now a strategic rule for survival.

What does this mean in practice?

Product design starts not with Figma mockups, but with an OpenAPI/Swagger specification. You must describe all capabilities of your service as a set of programmatically callable functions (endpoints).
API completeness criterion: Your API must be so complete that it can reproduce 100% of the functionality of your web or mobile interface. If you need hacks or direct database access for the frontend—your API is incomplete. You are not ready for the agent era.
Business implication: Your product becomes a platform. Beyond your own frontend, dozens of partner interfaces, vertical solutions, or personal user agents can be built on top of your API. You are no longer selling an interface—you are selling pure functionality.

Dogma Two: The “Human / AI” Switch Is the Key to Evolution

This is the most subtle and critical point. Giving an AI agent the same JSON response as your mobile app is a serious mistake. Their needs are radically different.

A human needs an answer: concise, structured, final.
“Your balance is 1,250 rubles. Last transaction: deposit of 5,000 rubles on 12.05.2024.”
An AI agent needs information and context: maximum detail, raw structured data, on top of which it can make decisions and formulate responses for the human. Not just a balance, but transaction history, spending categories, trends, next-month forecasts—taking into account known user plans (for example, an upcoming trip).

Example:

Human query: “What tasks do I have tomorrow?”
API response (Human mode):

{ "tasks": [ { "name": "Prepare report", "time": "10:00" }, { "name": "Team call", "time": "15:00" } ] }
API response (AI mode):

{ "tasks": [full task objects with all fields: description, priority, attachments, comment history, related projects, metadata], "user_context": { "calendar_events": [related events], "recent_activity": [project activity], "focus_time_slots": [when the user typically does deep work] } }

An AI receiving the second response can say:
“You have report preparation scheduled at 10:00 and a team call at 15:00. Since the call relates to Project Alpha, I recommend reviewing comments in task #XYZ beforehand. I also see that you have no meetings from 11:00 to 13:00—an ideal window for deep work on the report. Template files are already attached to the task.”

BIG MONEY FROM SMALL SAVINGS: HOW THIS TREND OPENS NEW MARKETS

This is where the Machine-First trend meets hard business logic. Large enterprises are not just “better customers.” They are ideal environments for AI agents, where saving a few cents per operation—multiplied by millions of operations—produces results that justify massive automation investments.

Real-world example: employee feedback platforms.

The idea that “frontline workers see the real problems” is well known. The traditional tool—anonymous surveys twice a year—is archaic: slow, context-free, and averaged.

The new solution is an API-first service built primarily for AI agents:

Integration: The service is not introduced as “another button” in a corporate portal, but as an API accessible to all internal systems. A Slack bot, a Jira plugin, a time-tracking form—all become points for collecting contextual feedback.
Role of the AI agent: An employee writes in Slack: “The copier on the third floor broke again—fifth time this month!”
Their personal AI agent, trained on corporate policies, automatically structures the complaint, enriches it with context (department, role, history), and sends it via API to a problem-management system (e.g., ServiceNow).
Scale effect: This is not a single ticket—it is a continuous stream of structured data about thousands of micro-issues: buggy software, uncomfortable chairs, broken equipment, illogical approval chains. An AI agent on the platform side analyzes this flow, identifies systemic issues, and generates reports not for HR, but for operations directors or the CIO.
Savings: Fixing just one systemic issue can save hundreds of lost work hours per year. At enterprise scale, savings reach millions of dollars. Paying $100,000 per year for such a platform is not a cost—it is a brilliant investment.

This is exactly how the FastFounder project mentioned earlier works. It doesn’t just “collect feedback.” It creates a machine-readable channel from employees directly to systems that can actually solve problems—bypassing the distorting filters of human hierarchy. And it does this via API, because the main “user” here is not the employee or even the manager, but the AI agent that analyzes, classifies, and escalates information.

WHAT TO DO RIGHT NOW

If your business is a digital service, your next product meeting should start with three questions:

Can our current API reproduce 100% of our frontend functionality? If not, the next quarter’s roadmap should focus on fixing this—starting with routine operations.
Do we provide more context to machines than to humans? You need separate API responses for agents, with extended metadata, history, related entities, and alternative data views.
Who is our “ideal customer”—a human with a browser or an autonomous agent? The answer determines architecture, monetization, and marketing. You may need a separate, premium “For AI Agents” plan with higher limits and priority API support.

The era of the human user is not over—but it has split. On one side are creative, exploratory, emotional interactions where humans remain central. On the other is a massive, routine, second-by-second digital traffic flow that will be captured by autonomous agents.

Your place in the future depends on which side you build your product’s foundation today.

Services that keep polishing the façade while ignoring the machine core will become digital ghosts—beautiful, but empty. Those who make their APIs complete, intelligent, and welcoming to AI agents will become the neural nodes of a new autonomous economy.

The choice, as always, is yours. But time is rapidly running out.

Bureau of Management Systems Design