Everyone's talking about building AI agents to automate work, and it's easy to see why. The idea of an autonomous agent handling frontline support, answering questions, and freeing up your team sounds pretty great. But when you start looking into how to actually build one, you’re suddenly swimming in a sea of technical jargon: APIs, agentic workflows, orchestration, LLMs. It can be a little overwhelming.
This guide is here to cut through the noise. We’re going to break down three of the most common building blocks, OpenAI’s AgentKit, the OpenAI API, and the Gemini API, from a practical, business-first perspective. We won’t be talking about code. Instead, we’ll focus on what really matters to a support leader: how long this will take, what it will cost, how much control you have, and ultimately, the experience you give your customers.
Understanding the building blocks
Before we dive in, let's get on the same page. Think of building an AI agent like making dinner. You can choose a meal kit with pre-portioned ingredients and instructions (that's AgentKit), or you can go to a specialty grocery store and buy raw ingredients to create a recipe from scratch (that's the OpenAI or Gemini APIs). Each approach gets you to a finished meal, but the effort, cost, and final result are worlds apart.
What is OpenAI AgentKit?
OpenAI’s AgentKit is OpenAI's toolkit designed to make building AI agents a bit simpler. It’s not a finished product you can use out of the box, but a set of components to help developers build faster. Its main parts are:
-
Agent Builder: A visual, drag-and-drop canvas for mapping out how an agent thinks and acts.
-
ChatKit: Pre-built pieces to help you stick a chat window on your website or app.
-
Connector Registry: A way to hook your agent up to a few outside tools and data sources.
Think of it as a sophisticated Lego set. It gives you the specialized pieces to build something cool, but you're still the one who has to put it all together, make sure it doesn't fall over, and figure out where it fits in your house.
A chart showing the relationship between Agent Builder, ChatKit, Evals, and Connectors in the AgentKit vs OpenAI API vs Gemini API comparison.What is the OpenAI API?
The OpenAI API is the raw, foundational engine that powers everything from ChatGPT for customer service to AgentKit itself. An API (Application Programming Interface) is just a way for one piece of software to talk to another. Using the OpenAI API directly gives a developer access to the core AI model, like GPT-4o. From there, they have total freedom to build whatever they can imagine. It’s the "bring your own everything" approach, you get the engine, but you have to build the entire car around it yourself.
What is the Gemini API?
The Gemini API is Google's direct competitor to the OpenAI API. It’s another foundational AI engine that provides the core intelligence for building custom applications. Developers might choose Gemini over OpenAI for certain jobs. For instance,
The build process: Speed and ease of use
So, you've decided you want an AI agent. How long is it going to take to get it in front of customers? The answer really depends on the path you choose.
AgentKit: Faster prototypes
AgentKit is definitely built for speed. Its visual canvas lets a product manager or a tech lead map out a workflow without writing code. This is perfect for building quick prototypes and getting an early version of your agent up and running.
But there’s a catch. While it’s faster to start, you’re pretty much locked into the OpenAI ecosystem. And more importantly, you’re still responsible for everything that comes after the prototype. You need to figure out where to host it, how to monitor its performance, how to keep it secure, and how to make sure it doesn’t crash when your ticket volume spikes. It gets you to the starting line faster, but you still have to run the whole race on your own.
OpenAI & Gemini APIs: The full engineering project
Using an API directly isn't a weekend project; it's a full-on product development cycle. This route requires a dedicated engineering team to:
-
Build the user interface from the ground up.
-
Manage the conversation's memory and history (often called state management).
-
Code every single connection to your internal tools.
-
Write all the logic that tells the agent what to do and when to do it.
The upside is complete control. You can build an agent that is perfectly tailored to your exact needs. The downside? It’s a long, expensive, and demanding process. You're not just building an agent; you're building and maintaining a new piece of software for your company.
The third way: Managed platforms for support teams
What if you could skip most of the build process? This is where managed platforms come into the picture. Instead of handing you a toolkit or a raw engine, they provide a solution that’s ready to go and designed specifically for a job like customer support.
A platform like eesel AI is built for this exact purpose. It’s a simple, self-serve solution where you can be live in minutes, not months. With one-click helpdesk integrations for tools like Zendesk, Freshdesk, and Intercom, it slots right into your existing setup without any complicated API work or developer time. It’s the most direct path from an idea to a fully functional AI agent that’s actually helping your customers.
Flexibility and control
An AI agent is only as good as what it knows and what it can do. Here, the differences between building it yourself and using a ready-made platform become even clearer.
Model lock-in vs. a multi-model strategy
AgentKit is designed to work best with OpenAI's models. This is fine if you're all-in on OpenAI, but it means you're tied to one provider. As developers often discuss, different AI "brains" are better at different things. You might want Gemini for summarizing a long customer complaint, another model for writing a sensitive, empathetic response, and GPT-4o for a quick, factual answer.
Building with an API gives you the freedom to pick and choose, but your team has to build and maintain the connections to each model. A platform like eesel AI solves this problem by being model-agnostic. It handles the complexity for you, so you can always use the best model for the job without being stuck with a single company.
Unifying knowledge: How the platforms get answers
This is one of the biggest challenges in building a useful agent. If you're using an API, your engineers have to build a complex system (you might hear the term Retrieval-Augmented Generation, or RAG) just to feed the AI information from your help center, internal wikis, and other docs. AgentKit offers a basic file search tool, but it doesn't have deep, automatic integrations with all the places your company knowledge lives.
This is a real strength of eesel AI. It instantly unifies your knowledge by training on your past support tickets, connecting directly to your Confluence and Google Docs, and even automatically suggesting new knowledge base articles from successful ticket resolutions. This creates an expert agent right out of the box that already knows your business.
Custom actions
A great support agent doesn't just answer questions; it solves problems. This means taking action: looking up an order status, processing a refund, or routing a ticket to the right person.
With APIs, your developers can code any action you can dream of, but each one is its own custom project. AgentKit’s connector framework is a start, but it can be a bit rigid. In contrast, eesel AI has a fully customizable workflow engine. Using a simple prompt editor, you can define your agent's exact persona, tone of voice, and the specific actions it can take, from looking up an order in Shopify to escalating a conversation to a human agent.
From prototype to production
Getting an agent built is only half the battle. You also have to launch it in a way that doesn't cause chaos.
Testing with confidence
How can you be sure your new AI agent won't go rogue on its first day? AgentKit and other toolkits have some evaluation tools, but they’re mostly for developers. If you're building with an API, you have to create your entire testing framework from scratch.
This is where eesel AI’s powerful simulation mode really shines. You can safely test your AI agent on thousands of your historical support tickets in a private sandbox. It gives you an accurate forecast of its resolution rate and shows you exactly how it would have responded to real customer issues, all before a single customer ever talks to it. This takes the risk and guesswork out of launching an AI.
Unpredictable API costs vs. transparent business pricing
The pricing for OpenAI and Gemini APIs is based on "token" usage, which is basically how many words the AI processes. This creates a variable, unpredictable operational cost. How can you possibly budget for your support costs when they can swing wildly depending on ticket volume and how chatty your customers are?
This is a huge headache for business leaders. eesel AI gets rid of this problem with transparent and predictable pricing. Our plans are based on the features and capacity you need, with no per-resolution fees. You'll never get a surprise bill after a busy month. It's a pricing model built for business planning, not for developers counting tokens.
Gradual rollout and continuous improvement
You probably don't want to flip a switch and have an AI agent handling 100% of your tickets overnight. A safe rollout is a gradual one. With eesel AI, you have total control. You can start small by having it handle just one simple ticket type, like password resets. It will confidently pass everything else to your human team. As you get more comfortable, you can expand what it does. Our actionable reporting also points out gaps in your knowledge base, giving you a clear roadmap for what to improve next.
Pricing breakdown
Cost is a big factor in any business decision. Here’s a simple breakdown of how these different approaches compare.
-
OpenAI AgentKit: The toolkit itself is free, but you pay for every action it performs through the standard API pricing. This means every conversation, every tool it uses, and every step in a workflow consumes tokens and adds to your monthly bill.
-
OpenAI & Gemini APIs: When you use the APIs directly, you pay for what you use. This gives you fine-grained control but makes budgeting a major challenge.
A screenshot of the AgentKit pricing page, relevant to the AgentKit vs OpenAI API vs Gemini API cost discussion.| Model Provider | Model Example | Price (per 1 million tokens input) | Price (per 1 million tokens output) |
|---|---|---|---|
| OpenAI | GPT-4o | $5.00 | $15.00 |
| Gemini 1.5 Pro | $3.50 | $10.50 |
You can always check the latest numbers on the official pricing pages for OpenAI and Google Cloud.
This variable cost is a huge departure from the predictable, tiered plans offered by a platform like eesel AI. With a bundled solution, you get everything, the platform, the AI models, the integrations, and the analytics, for a simple, flat monthly fee. It makes budgeting so much easier.
Choosing the right path for your team
So, which path is right for you? It all comes down to your goals, your team, and your timeline.
-
APIs (OpenAI & Gemini): This is the road to take if you want maximum power and flexibility, but it comes with the price of a significant, ongoing investment in a dedicated engineering team. It’s best for tech companies building AI into the core of their product.
-
AgentKit: This is a good middle ground for developers who want to build a prototype quickly within the OpenAI world. However, it's a toolkit, not a complete, ready-to-go solution for support automation.
-
Managed Platforms: For most business teams, especially in customer support, this is the fastest, safest, and most cost-effective way to get results.
For support leaders who want the power of a custom AI agent without the complexity, risk, and unpredictable cost of building it from scratch, eesel AI is the clear answer. It’s the production-ready platform that lets you automate support, not manage another engineering project.
Ready to see what a production-ready AI agent can do for your support team? Start your free trial with eesel AI and you can be live in minutes.
Frequently asked questions
AgentKit is a toolkit with pre-built components to speed up development, whereas the OpenAI API and Gemini API are raw, foundational AI engines. Using the APIs directly means building everything from scratch, while AgentKit provides some structure.
AgentKit allows for faster prototyping due to its visual builder but still requires significant engineering for production. Using OpenAI or Gemini APIs directly involves a full-scale engineering project, taking much longer but offering complete control.
Both AgentKit and the direct APIs charge based on "token" usage, leading to variable and often unpredictable operational costs. Managed platforms, like eesel AI, on the other hand, typically offer transparent, flat-rate business pricing.
AgentKit is primarily tied to OpenAI models. While using the raw APIs gives you the freedom to integrate multiple models, your engineering team must build and maintain all those connections themselves.
With raw APIs, engineers must build a complex system (RAG) to unify knowledge. AgentKit offers basic file search but lacks deep, automatic integrations with diverse company knowledge sources like help centers or internal wikis.
Direct API use allows for coding any custom action, but each is a bespoke project. AgentKit provides a connector framework which can be rigid. Managed platforms often offer customizable workflow engines for defining actions easily.
