How APIs Work and Why They Matter for AI

There's a piece of infrastructure that touches nearly every digital interaction you have, and most people couldn't name it. You used it this morning when your weather app pulled a forecast. You used it when your bank app checked your balance. You're almost certainly using it right now through whatever brought you to this page. It's the API—the application programming interface—and for something so foundational, it remains oddly invisible in public discourse about technology.

IBM's Amanda Downie recently walked through the basics in a video for IBM Technology, and while the explainer format targets a general audience, the material is worth sitting with. Because APIs aren't just plumbing. Especially right now, with AI reshaping how software gets built, APIs are increasingly the terrain where interesting questions about access, power, and security actually play out.

The Basic Architecture, Without the Hand-Waving

An API is, at its simplest, a set of rules that lets different software applications talk to each other. Downie describes it as "the invisible digital glue that's holding the modern world together"—which is marketing-adjacent phrasing, sure, but it's also accurate. The weather app on your phone doesn't contain weather data. It sends a request via an API to a weather service's server, gets data back, and displays it. The app and the server never need to know anything about each other's internal architecture. They just need to agree on the protocol.

That abstraction is the point. APIs let developers build on top of existing services without needing access to the underlying code. They're why a startup can integrate Google Maps without building mapping infrastructure, why Stripe can process payments for thousands of apps without any of them touching Stripe's database directly, why Slack integrations exist at all.

The web API landscape has evolved into several distinct categories. Open APIs are public-facing—defined endpoints, documented request and response formats, accessible to anyone. Partner APIs sit behind login credentials and connect specific business relationships, often through self-service developer portals. Internal or private APIs are hidden from external users entirely, used by organizations to wire together their own systems and teams. Composite APIs bundle multiple endpoints into a single call, which matters particularly in microservices architectures where a single user-facing action might require coordinating a dozen backend services.

Beyond web APIs, there are database APIs and operating system APIs—less glamorous, but equally load-bearing. The whole stack, from your OS to your cloud-hosted SaaS tool, is threaded with these interfaces.

The Architecture Alphabet Soup

Here's where things get genuinely complicated, and where the explainer-video format starts to show its limits. Downie touches on the major architectural styles: SOAP (Simple Object Access Protocol), various flavors of RPC (Remote Procedure Call, including XML-RPC, JSON-RPC, and Google's gRPC), WebSocket, GraphQL, and REST (Representational State Transfer).

Each carries different tradeoffs. REST is the dominant paradigm for web APIs—stateless, HTTP-based, human-readable. It's why most public APIs feel roughly similar to work with. SOAP is older, stricter, still prevalent in enterprise and financial systems where that rigidity is a feature rather than a bug. gRPC, developed by Google, is faster and better suited for internal service-to-service communication at scale. GraphQL gives clients more control over exactly what data they fetch, which reduces over-fetching but adds complexity to schema design.

These aren't just technical footnotes. The choice of architecture shapes what developers can build on top of a platform, how much flexibility they have, and—critically—who can participate. A public REST API with good documentation is an open invitation. An undocumented internal SOAP interface is effectively a closed door. The architecture is a policy decision as much as a technical one.

Where AI Enters the Picture

The AI dimension is where this conversation gets most interesting, and where IBM's framing is worth both taking seriously and interrogating a little.

Downie offers a useful metaphor: when you're using AI, "APIs can act like a straw that feeds a company's private information into an AI model so it can answer specific questions." The image captures something real. Retrieval-augmented generation—the approach where an AI model queries external data sources to ground its responses—runs on APIs. An enterprise AI assistant that can tell you about your company's Q3 inventory numbers is doing so because an API is piping that data into the model's context window.

But the implications go further than that. Downie notes that APIs allow AI to "do more than just talk. It can act on your behalf, like booking a flight or sending an email." That's the agentic AI moment we're currently moving into. The same interface that lets your weather app fetch a forecast can let an AI agent actually do things in the world—submit forms, trigger workflows, make purchases. The API becomes not just an information conduit but a capability conduit.

This is where security stops being a checkbox item and becomes genuinely high stakes.

Security as a First-Class Concern

Downie covers API security practices—authentication and authorization, encryption in transit, rate limiting—in a way that reads as thorough for a general explainer. But the reality is that API security is one of the most active attack surfaces in modern software. According to Gartner research, APIs have become a leading attack vector, and the landscape of API-specific vulnerabilities (broken object-level authorization, excessive data exposure, security misconfigurations) is distinct enough that OWASP maintains a dedicated API Security Top 10 list separate from its general web application list.

Rate limiting prevents abuse, yes. But it doesn't address what happens when an authenticated, authorized request is doing something it shouldn't. The principle of least privilege—giving any API client access to only exactly what it needs—matters enormously here, and it's a design discipline that's easy to skip when teams are moving fast.

When APIs are feeding AI agents with action-taking capabilities, the blast radius of a misconfiguration expands considerably. An API that exposes a booking endpoint to a human user through a UI has certain implicit safeguards—the human has to actually click things. An AI agent calling that same endpoint programmatically, at scale, at 3 AM, does not.

The Design Side Nobody Romanticizes

There's a quieter part of the API story that tends to get lost in the innovation framing: the unglamorous work of API design and maintenance. Downie mentions using clear naming conventions, writing comprehensive documentation, and versioning APIs to manage changes over time. These are not exciting to talk about. They are the difference between an API ecosystem that works and one that's a graveyard of confused integrations and breaking changes.

Versioning in particular is a governance story. When a company deprecates an API version, every developer who built something on top of it is now on the clock. How much notice they get, whether there's a migration path, whether the new version preserves backward compatibility—these decisions ripple outward through entire ecosystems of dependent projects. It's one of the places where the "open" in open API can feel more nominal than real.

The Infrastructure Nobody Sees

What I find genuinely interesting about IBM's explainer is not that it reveals anything new to developers, but that it reflects a moment when this infrastructure is becoming consequential enough to need explaining to everyone. APIs have been the plumbing of software for decades. But as AI agents start taking actions through those interfaces, as more critical systems become API-accessible, and as the line between "fetching data" and "doing things" continues to blur—the plumbing matters more than ever.

The abstraction that makes APIs so useful—the way they let you build on services without knowing how they work internally—is also what makes them opaque. You might be using a dozen APIs right now with no idea who maintains them, under what terms, or how they'd behave if the company behind them pivoted or shut down. That's been true for years. AI just makes the stakes of that opacity higher.

The question isn't whether APIs will continue to shape the digital world. They already do, invisibly, constantly. The question is whether the people building on top of them—and increasingly, the AI systems acting through them—will be working with that architecture thoughtfully or just assuming it holds.

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By Dev Kapoor, Open Source & Developer Communities Correspondent, Buzzrag