From Chat to Agents: Choosing the Right AI API for Your Next App
If you’re building an AI-powered app today, chances are high you’ve already used—or at least experimented with—OpenAI’s Chat Completions API. It’s the foundation behind the AI boom, and for good reason: you send a message, get a response, and maybe throw in a tool call if you’re feeling fancy.
But if your app is starting to do more—more tools, more steps, more logic, more personalities—you might be wondering: is this really scalable? Should I be building my app on something more structured? Should I be using OpenAI’s new Agentic Framework?
We recently had to make this exact decision while building our own app suite here at Wild Apps. What follows is the distilled version of what we learned—so you can skip the headaches, and choose the right stack for your project from the beginning.
The Problem with Staying Basic
Let’s say you start with Chat Completions. It’s great for a basic assistant. Maybe it answers some FAQs, pulls data from a contact list, or writes a quick email. You might even throw in OpenAI’s function calling feature so the model can execute specific tasks.
But then, your app grows. You want your assistant to do a series of steps: search something, retrieve a file, calculate something, and then send a message. You want to build specialized agents—a sales bot, a finance bot, a project manager—and have them talk to each other, or delegate work. You want to debug why a request went sideways. You want to add memory, guardrails, and maybe let your users interact with the agents via chat, voice, even SMS.
Suddenly, your once-elegant backend is littered with routing logic, state handlers, tool pipelines, and a graveyard of prompt files. You’re no longer building your app—you’re building an orchestration engine.
This is exactly what the Agentic stack was built to fix.
What Is the Agentic Stack?
In early 2025, OpenAI released the Responses API and an open-source Agents SDK—together known as the “Agentic Stack.” It’s their answer to the growing complexity developers face when trying to turn powerful models into real-world applications.
Think of it as the next evolution of Chat Completions—designed for apps that need more than just a talking box.
With the Agentic Stack, you can:
-
Define multiple agents, each with their own tools and personality
-
Let agents delegate to each other using built-in handoffs
-
Use built-in tools like web search and file search without third-party hacks
-
Add guardrails to validate input/output and ensure safety
-
Visualize and trace every step your agent takes—no more guesswork
-
Build flows where agents reason over multiple steps in a single API call
It doesn’t try to reinvent how you build apps—it just replaces the mess of glue code with a minimal, clean framework that handles orchestration for you.
So When Should You Use It?
That depends on what you’re building. If your app is lightweight—a single assistant that mostly answers one-off questions or triggers simple tools—then Chat Completions may be all you need. It’s fast, easy to integrate, and very well supported.
But if you’re doing any of the following, you should seriously consider going Agentic from the start:
-
You want your app to have multiple specialized agents (like different bots for sales, marketing, operations, etc.)
-
Your agent needs to perform multiple steps in a row (e.g. find a contact, summarize a note, then schedule a meeting)
-
You need to plug in external data or documents and let the AI reason over them (RAG-style search)
-
You want to trace and debug what the AI is doing, especially in production
-
You’re planning to scale the AI features over time—adding memory, tools, or workflows later
The beauty is: you don’t need to go all-in from day one. You can start with a single agent, doing simple tasks, and scale up. The Agentic framework is modular, so adding a new agent or tool is usually just a few lines of code.
What About the Cost?
The good news is: there’s no extra cost to using the Agentic stack itself. You still pay standard token rates for the model, and additional fees for specific built-in tools (like web or file search). In practice, we found that the Responses API is often more efficient than rolling your own loops, since a single API call can handle multi-step reasoning instead of requiring three or four chained calls.
The cost difference, then, isn’t about the dollars—it’s about the time. Time saved writing less orchestration code. Time saved debugging weird AI behavior. Time saved reusing patterns that the SDK already solves.
If you’re building an app that you plan to maintain and evolve, that time adds up fast.
Real Talk: What’s the Catch?
The biggest caveat right now is that the official SDK is Python-only. If your backend is all JavaScript or you’re hosting on serverless platforms like Netlify (like we are), you’ll either need to hit the Responses API directly over HTTP or spin up a lightweight Python service to handle orchestration.
That said, OpenAI says a Node SDK is on the way—and the Responses API is fully available via REST, so you’re not blocked.
The other thing to watch for is that some built-in tools (like code execution or advanced memory) are still evolving. But honestly? It’s moving fast, and OpenAI has made it clear: this is the future. The old Assistants API is being phased out by mid-2026. The Agentic stack is what they’re betting on.
Our Verdict
At Wild Apps, we decided to go Agentic early. It felt a little heavier at first—learning a new SDK, setting up delegation flows—but once we got it running, we realized how much it simplifies the hard stuff. It let us focus on what makes our product unique, not the plumbing.
If you’re serious about building agent-based apps—not just wrappers around GPT, but tools that think, act, and collaborate—then the Agentic stack is worth the investment.
Even if you’re launching with something basic, having that foundation in place means you won’t need to rebuild when your users inevitably ask for “just one more thing.”
Because you know they will.
