AgentKit vs Make: Which AI automation tool is right for you?

If you’re looking into building “AI agents” to automate your work, you’ve probably run into two big names: OpenAI’s AgentKit and Make. They both promise to handle complex tasks for you, but they come at it from completely different directions. AgentKit is a specialized toolkit for building smart, conversational workflows right inside the OpenAI ecosystem. Make, on the other hand, is a massive automation platform built to connect just about any web app you can think of.

So, how do you pick one? This guide will walk you through the real differences between AgentKit vs Make. We'll get into their AI smarts, how they connect to other tools, what they cost, and where they each shine. By the end, you should have a much clearer idea of which one fits what you’re trying to do.

What is OpenAI's AgentKit?

Think of AgentKit as OpenAI's all-in-one toolbox for building, launching, and tweaking AI agents. It’s not just one thing, but a few powerful parts that click together:

• Agent Builder: This is your visual workspace, a drag-and-drop canvas where you can map out how your agents will work and manage different versions.

• ChatKit: A handy kit for dropping a polished, customizable chat window into your app or website without a bunch of coding headaches.

• Evals & Guardrails: These are built-in tools for testing how well your agent is doing and setting up safety rules to keep it from going off the rails.

It’s really made for teams who are already using OpenAI and want to build sophisticated chatbots for customers, without having to stitch together a bunch of different tools.

What is Make?

You might remember Make by its old name, Integromat. Its whole mission is to connect different apps and services to automate repetitive tasks, all without you having to write any code.

In Make, you build “scenarios” by visually linking together modules from a huge library of over 1,600 applications. While it wasn't specifically built to be an "AI agent" creator, its flexible system of routers and filters lets you design complex, multi-step AI workflows that act a lot like an agent. It’s a great option if you need to automate backend processes that touch a bunch of your favorite software tools.

A detailed comparison

Alright, let's get into the nitty-gritty. Both platforms give you a visual builder, but what you can actually build and how you go about it are pretty different.

AI capabilities and workflow logic

AgentKit's focus on agent logic

AgentKit was born to create agents that can reason, use tools, and carry on a conversation. Its visual builder is all about setting up how different agents team up, passing information between them to get something done. This is perfect for building structured, agent-powered experiences.

That said, some early users have noted that the workflows can feel a bit rigid. AgentKit often depends on a step-by-step path, meaning you have to manually connect "if/else" nodes for every little decision point. It doesn't quite have that magic of an AI that can look at a list of tools and pick the right one on its own. Its biggest strength is its built-in evaluation feature, which lets you test your agent's answers against a set of data to see how it performs before a customer ever interacts with it.

Make’s approach to agent-like workflows

Make gets there a different way. It uses a powerful mix of modules and routers to create branching logic. This is fantastic for task-based automation. For example, you could easily set up a flow that says, "When a new email with an invoice shows up, have an AI model pull out the key details, then pop them into a new row in a Google Sheet."

But this is also where you see the trade-off. Make doesn't have a built-in system for agent-specific ideas like long-term memory or choosing its own tools. You have to build the "thinking" part of the process yourself, step-by-step. AgentKit has those concepts baked in, even if they have their own set of rules.

The eesel AI alternative for support automation

Here’s the thing: general-purpose tools can start to feel clunky when you're dealing with something as specific and important as customer support. The rigid flows in AgentKit aren't great for the wild, unpredictable nature of customer questions, and building out logic in Make for every possible support issue just isn't realistic.

This is where a specialized platform like eesel AI comes in. It offers a fully customizable workflow engine built just for support conversations. You get fine-grained control to automate certain types of tickets (like questions about order status) while making sure the tricky stuff gets escalated to your human team. Best of all, it actually learns from your past support tickets, so its logic is grounded in how your team already solves problems.

A workflow diagram illustrating how a specialized tool like eesel AI automates the customer support process from ticket analysis to resolution, a key point in the AgentKit vs Make discussion.

Ecosystem and integrations

AgentKit: Deep but narrow

Where AgentKit really pulls ahead is its seamless, native connection to OpenAI's own models (like GPT-5) and services. Everything is designed to work together right out of the box.

The downside is that you're pretty locked into their world. You can't easily swap in a model from Anthropic or Google to see which one works best for what you're doing. It does support some integrations for enterprise data sources like Google Drive and SharePoint, but its list of pre-built app connectors is tiny compared to what else is out there.

Make: Broad and flexible

Make's standout feature is its massive library of over 1,600 native app integrations. If you use a popular piece of software, there's a very good chance Make has a connector for it. This makes it incredibly useful for automating work across your entire tech stack.

You can also use its generic HTTP module to call any AI model you want. This gives you total freedom, but it does mean a bit more of a technical setup compared to AgentKit's simple click-and-connect style.

Why deep integration matters more for support teams

For a customer support team, having 1,600 generic connectors isn't nearly as useful as having deep, one-click integrations with the few tools you use all day, every day. eesel AI instantly connects to your help desk (like Zendesk, Freshdesk, or Intercom), your internal knowledge bases (like Confluence or Google Docs), and your chat tools (like Slack). More importantly, it doesn't just connect, it trains its AI on your historical tickets and knowledge base articles to understand your business right from the start.

An infographic illustrating how eesel AI centralizes knowledge from different sources to power support automation, a contrast to the general tools in the AgentKit vs Make comparison.

Deployment and user experience

AgentKit: All about the polished front-end

This is where AgentKit really shines. With its ChatKit component, you can get a beautiful, responsive, and fully branded chat widget live on your website in minutes. It’s a huge plus if you’re building a customer-facing agent and want it to look professional without a ton of web development work.

The whole platform is managed by OpenAI, which makes setup and maintenance easier. The catch is that it's a closed-source product, so you have less control over your data and where it's stored.

Make: Focused on the backend

Make is a behind-the-scenes operator. It excels at automating the invisible workflows that don't need a pretty interface. Its visual debugger is incredibly helpful for figuring out what went wrong in a complex scenario and seeing exactly where a process broke down.

Like AgentKit, it's a closed-source, cloud-only platform. This could be a non-starter for companies with strict data or security rules that require a self-hosted option.

Deploying with confidence is key

Pushing an AI agent live to your customers can be a little nerve-wracking. A single bad experience can do more harm than good. While AgentKit gives you tools to evaluate your agent, eesel AI takes it a step further with a powerful simulation mode. You can test your AI on thousands of your actual past support tickets in a safe sandbox environment. This gives you an accurate forecast of its performance, resolution rate, and cost savings before a single customer talks to it, helping you launch with confidence.

The eesel AI simulation dashboard, a key differentiator for specialized platforms in the AgentKit vs Make discussion.

Pricing showdown

It's really important to understand how you'll be charged before you commit to a platform, as the two models are very different.

AgentKit pricing

AgentKit doesn't have its own subscription fee. Instead, the cost is tied directly to how much you use OpenAI's API models.

• How it works: You pay for the input and output tokens your agents use.

• The catch: This pricing can be all over the place. A complex agent or a busy month could lead to a surprisingly high bill, making it tough to budget.

Make pricing

Make charges you based on "operations." A single operation is one action performed by one module. So, a 10-step scenario that runs once uses up 10 operations.

• Free: 1,000 operations/month

• Core: $9/month for 10,000 operations/month

• Pro: $16/month for 20,000 operations/month

• Teams: $29/month for 40,000 operations/month

• The catch: If you have workflows that run often or have a lot of steps, these costs can climb pretty quickly.

A more predictable alternative

Most businesses, especially in customer-facing roles, can't really deal with unpredictable costs. That’s why eesel AI's pricing is set up differently. It offers simple plans based on a set number of AI interactions per month. There are no extra fees per ticket resolved, so you don't get penalized for being successful or having a busy month. This straightforward model makes budgeting a breeze and helps you avoid any nasty surprises on your bill.

A view of the eesel AI pricing page, which offers a predictable alternative to the models discussed in the AgentKit vs Make debate.

Choosing the right tool for the job

So, what's the verdict in the AgentKit vs Make matchup? Honestly, there isn't a single winner. It’s all about picking the right tool for what you're trying to accomplish.

• Choose AgentKit if: You need to build a polished, customer-facing chatbot and you're already all-in on the OpenAI ecosystem. Its ChatKit and evaluation tools are perfect for that.

• Choose Make if: Your main goal is to automate backend processes by stringing together a bunch of different SaaS apps. That huge integration library is its biggest strength.

The big catch with both? Neither platform was built from the ground up to solve the tricky problems of customer support automation, which demands deep knowledge integration, specialized workflows, and a safe way to deploy.

Beyond AgentKit vs Make: The specialized alternative for support and IT teams

If you're reading this because you need to automate customer support or internal help desk tasks, a general tool will only take you so far.

eesel AI is designed specifically for this world. It can go live in minutes, not months, because it instantly connects to and learns from all your existing knowledge, your past help desk tickets, Confluence pages, Google Docs, and more. With a powerful workflow engine and a risk-free simulation mode, you get full control over your automation and complete confidence in how it will perform.

Ready to see how a purpose-built AI support platform can change how you work? Try eesel AI for free.