On January 7, 2026, Dr. Wayne Liu, president and chief growth officer of Perfect Corp., delivered a presentation at the Consumer Electronics Show (CES) titled “API-First Innovation: Scalable AI for the Modern Beauty Shopper,” discussing the many different ways that AI is impacting the beauty industry. In the process, he makes a case for our current agentic AI-driven moment as a logical culmination of the last 15 years of business and technology, and argues that API-first AI is imperative for businesses to become scalable and remain competitive.

Of course, not everybody is as bullish on API-first AI as an entrepreneur who regularly delivers keynote speeches on the potential of augmented reality (AR) and AI for delivering quality customer service. MPT Solutions referred to API-first AI as “a hidden cost crisis,” arguing that API costs can quickly spiral out of control. Chat history grows with each conversation, for one thing, which can cause token usage to spike exponentially. That’s not even mentioning the skill atrophy that can ravage a development team’s abilities. A freelance developer told MPT Solutions that 70% of their business is now made up of clients wanting trained programmers to debut something made by AI.

While it may have its drawbacks, like excessive rate limiting or exorbitant cloud-hosting fees, there have been plenty who have been using this new design strategy. From beauty professionals and retailers to customer service providers to logistics agencies, here are some examples of successful uses of API-first AI agents to motivate and inspire you as you build API-enabled AI products.

What Makes an AI Company API-First?

An API-first AI company designs its platform so that it’s built for AI systems, not just humans. The API is the primary interface through which intelligence is built, deployed, and operated, instead of a secondary integration layer released after the fact.

In an API-first AI architecture, every capability available in the product is exposed programmatically. This allows teams to build custom AI agents that understand their domain, customers, and edge cases while operating directly inside of production systems. The API is not limited to accessing data. It supports actions, permissions, events, and state changes in real time.

API-first AI platforms assume that intelligence will be made up of simple, composable patterns instead of using complex frameworks. Developers are able to connect models to live product context, trigger workflows based on behavioral signals, and create machine users that act autonomously within defined boundaries. This allows AI agents to act like operators, not just copilots — or “agentic instead of assistive”, as Liu puts it in his CES presentation.

The same APIs are able to power both internal features and external agent development. This alignment ensures that AI capabilities remain reliable, extensible, and future-proof, able to support new models, workflows, and interaction patterns without having to redesign the platform.

In this sense, API-first AI isn’t about exposing endpoints for AI features. It’s about building infrastructure where AI is the default way the system is extended and operated.

5 Examples of Successful API-First AI

1. PTV Logistics

PTV Logistics has leveraged API‑first AI to build PTV Mira, an interactive AI agent designed to simplify complex logistics operations. Unlike traditional business dashboards that are only able to display historical data, PTV Mira is also able to display real-time simulations and scenario analyses.

By exposing every core function, from routing and electric vehicle modeling to constraint management and scenario engines, the platform allows the AI agent to execute operations directly instead of just querying data. Users are able to ask questions using natural language like “What happens if volume increases 20% next Monday?” and receive actionable, prescriptive insights. This approach demonstrates how an API‑first design can turn powerful AI models into business intelligence that can be used immediately, without requiring users to understand the underlying algorithms.

2. Plain

Plain
Plain is a striking example of API‑first AI’s ability to provide customer support. Their platform exposes every function through GraphQL and REST APIs, making it possible for companies to build fully customized AI support workflows. Instead of relying on rigid, legacy support software, technical teams are able to create tailored AI agents that respond to complex queries, integrate with multiple systems, and automate processes at scale.

Companies like Vercel, Cursor, n8n, Raycast, Stytch, Sanity, Tinybird, and Sourcegraph have adopted Plain because its API‑first approach allows programmatic access to all of its support functions, giving teams flexibility to implement AI exactly where it adds the most value. This example illustrates how an API‑first architecture both accelerates deployment and enhances developer productivity in technically complex environments.

3. Perfect Corp

In the consumer technology space, Perfect Corp. has showcased the benefits of API‑first AI with its YouCam AI Agent and its accompanying API Innovation Suite. The platform enables brands to embed personalized, AI-driven beauty guidance directly into digital experiences.

By exposing AI functions as modular APIs, Perfect Corp. allows developers to integrate sophisticated AI capabilities like skin analysis, virtual try-on, and conversational recommendations into their own applications or e-commerce platforms. The API‑first design ensures that these AI features can be scaled across multiple endpoints and adapt quickly to evolving business needs, making it easier for brands to deliver real-time, interactive customer experiences.

4. Intercom

Intercom is widely recognized for its chat-first approach to customer engagement, making it a popular choice among product-led growth (PLG) and B2C companies. Its AI assistant, Fin, delivers strong conversational capabilities that are tightly integrated into Intercom’s messaging experience, helping teams automate common support interactions and guide users through in-product journeys.

From an API-first perspective, however, Intercom’s platform is more oriented toward conversation instead of extensibility. While APIs are available, meaningful access to advanced functionality often requires higher pricing tiers, and customization options for building custom AI agents or deeply embedding AI into external systems are relatively limited. As a result, Intercom works best for organizations that prioritize out-of-the-box chat experiences instead of programmable workflows.

For teams focused on rapid deployment, in-app messaging, and streamlined customer communication, Intercom’s approach can be highly effective. For more technical organizations looking to orchestrate AI across multiple systems or build custom agent logic, its chat-first design can become a limitation instead of an advantage.

5. Zendesk

Zendesk has been a staple of customer support since it launched out of a loft in Copenhagen in 2006, offering powerful tooling for compliance, reporting, and large-scale operations. Its platform supports complex organizational structures and regulatory requirements, making it popular with large enterprises using established support processes.

Zendesk approaches API-enabled AI from an enterprise-first perspective, integrating artificial intelligence into a mature platform designed for scale, governance, and long-term operational stability. Its APIs and AI agent capabilities are intended to extend and automate established support workflows, allowing organizations to introduce AI-assisted interactions while maintaining consistency across ticketing, reporting, and compliance systems.

Zendesk’s APIs enable software to access most of its core functionality, like ticket management, AI-assisted conversations, and workflow automation, which makes it possible to incorporate AI into existing processes instead of redesigning them from scratch. This approach aligns well with organizations that value predictability, centralized control, and incremental AI adoption over rapid change.

As a result, Zendesk is especially well-suited for enterprises wanting to leverage API-driven AI in a familiar system. Its main advantages lie in supporting API-enabled automation and AI augmentation at scale, especially for teams looking for continuity while investigating the possibilities of AI.

Why API-First AI Matters for Modern Platforms

API-first AI is no longer theoretical — it’s a practical, scalable strategy for building intelligent systems across industries. From PTV Logistics’ real-time operational simulations to Plain’s fully programmable customer support agents, and beyond, API‑first platforms demonstrate how exposing core functionality programmatically allows both internal teams as well as external developers to innovate, even if they’re lacking in technical expertise.

By prioritizing composable APIs, real-time actions, and extensible architectures, organizations can use AI agents that act as active participants, adapting to changing business needs while remaining reliable and future-proof. Whether the goal is to enhance customer experiences, automate complex workflows, or integrate AI deeply into existing processes, API‑first development provides a framework that’s able to scale as a company grows.