So, you're thinking about connecting a workflow automation tool like Make with an AI agent builder like OpenAI's AgentKit. On the surface, it makes a lot of sense. You could use Make's huge library of app connectors to do the backend work, while AgentKit handles the smart, conversational parts. The goal is to build powerful AI agents that can manage complex, multi-step tasks all on their own.
But what does it actually take to pull this off? The truth is, stitching these two powerful (but very different) platforms together isn't a simple plug-and-play operation. This guide will give you a real look at what Make integrations with AgentKit involve, including the messy bits and limitations, and show you a more streamlined way to build AI that gets the job done.
What are Make and OpenAI AgentKit?
To understand why this integration is so tricky, we need to look at what each platform was actually built for. They come from different schools of thought and solve different problems, and that’s where things get complicated.
What is Make?
You might remember Make as Integromat. It’s a well-established visual tool for automating workflows. Think of it as a digital switchboard operator for your apps. Its main strength is its massive ecosystem of over 2,500 app integrations, letting you connect things like your CRM, databases, and communication tools without having to write code.
Make has started adding AI features, but they feel more like an add-on to its main job: automating processes and shuffling data between applications. It's fantastic at handling the "if this happens, then do that" logic that keeps a business running.
What is OpenAI AgentKit?
OpenAI AgentKit, on the other hand, is a toolkit designed specifically for creating conversational AI agents. It’s all about mapping out an agent's reasoning, logic, and how it talks to people. It’s made up of the Agent Builder, a visual space for designing how an agent "thinks," and ChatKit, which helps you embed a chat UI into your product.
AgentKit is focused entirely on the conversation and the agent's intelligence, not on connecting to a bunch of backend systems. It gives you the tools to build the AI's brain but leaves it up to you to figure out how to feed it information from the rest of your business.
The promise and reality of Make integrations with AgentKit
So, why would you even want to connect these two? The idea is to combine their strengths. You'd use Make for the heavy lifting on the backend and have it trigger an AgentKit agent to handle the conversational piece of a task.
Let's imagine a customer submits a support ticket. In a perfect world, the process would look like this:
-
A new ticket comes in and kicks off a Make scenario.
-
Make grabs the customer's details from your CRM and their recent orders from Shopify.
-
Make bundles this info up and sends it over to your AgentKit agent through an API call.
-
The AgentKit agent, now with all the context it needs, figures out the problem and writes a perfectly personalized reply.
-
Make takes that reply and posts it back into your helpdesk, updating the ticket.
It sounds great, right? The problem is, you’re not really creating one smooth workflow. You’re building a rickety bridge between two separate islands. This setup forces you to manage logic, API calls, and error handling across two different platforms with two different interfaces. You might save some time on initial coding, but you'll pay for it later in complexity and maintenance. You end up with multiple points of failure that are a nightmare to track down and fix.
Key challenges of building Make integrations with AgentKit
While a talented developer could probably make this work, this DIY approach is filled with practical problems that can stall projects, make it hard to grow, and leave you with a system that creates more work than it saves.
Fragmented knowledge and context
An agent built in AgentKit has no idea what's going on in all the apps Make is connected to. To give the agent any useful context, you have to manually set up Make to pull specific bits of data and carefully pass them along in an API call. This is slow and clunky, and it seriously limits the agent's ability to find information on its own. In contrast, a unified platform like eesel AI is trained directly on all your connected knowledge sources, from helpdesk tickets to Confluence pages, without needing any complex API setups.
Lack of unified testing and simulation
You can test your Make scenario, and you can preview your AgentKit workflow, but there’s no way to run a simulation of the whole process from start to finish with real data. You’re basically flying blind, with no real way to know how well your automation will perform or to catch small errors before you launch. This is a huge risk that platforms like eesel AI are built to solve. With a powerful simulation mode, you can safely test your AI on thousands of past tickets before it ever touches a single customer conversation.
Weak governance and control
Trying to manage security, permissions, and agent behavior across two separate systems is asking for trouble. An agent built in AgentKit doesn't automatically follow the business rules or specific controls you might have in Make, which can create compliance and safety headaches. A dedicated AI support platform gives you a single dashboard to control everything, letting you define exactly which tickets the AI can handle, what actions it can take, and what knowledge it's allowed to use.
Complex maintenance and high overhead
With a two-platform setup, one small change can cause a domino effect. If an API in Make gets updated, it might break the data format your AgentKit workflow is expecting. This means double the maintenance work and requires you to have experts in both platforms just to fix things. Instead of making your operations simpler, you’ve just made them twice as complicated.
| Challenge | Impact of DIY Make + AgentKit Approach |
|---|---|
| Fragmented Knowledge | The AI has limited context and needs complex API calls to get data. |
| No End-to-End Testing | It's a high-risk launch; you can't predict performance ahead of time. |
| Weak Governance | It's tough to enforce consistent security and business rules. |
| High Maintenance | You have double the work to update and debug across two platforms. |
A simpler approach to Make integrations with AgentKit: Unifying workflows with an all-in-one AI platform
Instead of trying to tape two different tools together, what if you used a single platform that was designed from the start to bring knowledge, automation, and AI together?
This is exactly what eesel AI does. It’s not just an agent builder or a workflow tool; it’s a complete AI platform for support teams that manages everything in one place, solving the very problems the DIY approach creates.
-
All your knowledge, connected instantly. With eesel AI, you can connect your helpdesk, wiki, and other documents with one-click integrations. The AI automatically trains on everything, including your past tickets, so it has deep, useful context right from the beginning.
-
Go live in minutes with solid testing. eesel AI is built to be self-serve. You can sign up, connect your data sources, and run simulations on thousands of your historical tickets to get accurate performance predictions in minutes, not months. You don't even need to talk to a salesperson to get started.
-
Total control over your automation. A simple, visual workflow engine lets you define exactly what your AI should do. You can set its persona, limit its knowledge to certain topics, and create custom actions to look up order info or tag tickets, all without writing code or switching between platforms.
-
Seamless integration with your helpdesk. eesel AI plugs directly into tools you already use, like Zendesk and Freshdesk. It works with your existing workflows instead of making you build new ones from scratch.
Pricing comparison: Make and AgentKit vs. a unified platform
Of course, cost is a big piece of the puzzle. The DIY approach can lead to some nasty, unpredictable bills that are almost impossible to budget for.
Make pricing
Make charges you based on "operations." Every step in a workflow counts as an operation. A single support ticket could set off a workflow that eats up dozens of operations, making your costs scale quickly and without warning. Their plans include a free tier, with paid plans like the Core plan starting at $9/month for 10,000 operations.
OpenAI AgentKit pricing
AgentKit itself doesn't have a separate price tag, but you pay for all the OpenAI API and model usage underneath. Every question, every step in the agent's thought process, and every response consumes tokens. With powerful models like GPT-4o, these costs can add up incredibly fast, making it nearly impossible to guess what your monthly bill will look like.
eesel AI pricing: A unified alternative
eesel AI uses a clear, predictable model with a flat monthly fee for a certain number of AI interactions. A huge plus is that there are no per-resolution fees, so your costs won't spike just because you had a busy month. You get all the features you need in one plan, with pricing that lets you scale confidently.
| Platform | Pricing Model | Predictability | Best For |
|---|---|---|---|
| Make | Per-operation | Low to Medium | Teams with simple, low-volume workflows. |
| OpenAI AgentKit | Per-token (API usage) | Very Low | Experimental projects where budget isn't a concern. |
| eesel AI | Flat fee per interaction tier | High | Businesses that need predictable costs and scalable AI. |
Choose the right tool for your Make integrations with AgentKit
While connecting Make and AgentKit might be an interesting technical challenge, it results in a fragile, overly complex system that's a pain to manage, test, and scale for real-world support automation. The reality is that workflow tools are for backend processes, and agent builders are for crafting conversations. For customer support, you need a platform that does both without the fuss.
For teams that care about speed, control, and reliability, a unified, purpose-built platform is the most logical choice. A solution like eesel AI gives you all the power of an advanced AI agent without the integration headaches. It lets you focus on improving your customer experience, not on maintaining a Frankenstein's monster of a tech stack.
This video demonstrates a method for connecting an OpenAI agent to a vast number of automations, illustrating the concepts behind Make integrations with AgentKit.
Ready to see what a truly unified AI platform can do? Start your free eesel AI trial or book a demo to build a powerful AI agent for your support team in minutes.
Frequently asked questions
Users hope to combine Make's extensive app connectivity for backend processes with AgentKit's conversational AI capabilities. This aims to create powerful AI agents that can automate multi-step tasks while interacting intelligently with users.
Key challenges include fragmented knowledge across platforms, the absence of unified end-to-end testing, weak governance over AI behavior, and complex maintenance due to managing two disparate systems. These issues can lead to increased overhead and points of failure.
An agent in AgentKit has no inherent knowledge of Make's connected apps. Make must be manually configured to extract specific data and then meticulously pass it to AgentKit via API calls, a process that can be slow, clunky, and limit the agent's autonomy.
No, a significant limitation is the inability to run end-to-end simulations of the entire process using real data. You can test individual components, but there's no integrated way to predict overall performance or catch errors before deployment, leading to high-risk launches.
Make charges per operation, and AgentKit (OpenAI API usage) charges per token, leading to unpredictable and potentially high costs that scale rapidly. A unified platform like eesel AI typically offers more predictable flat-rate pricing for AI interactions, avoiding per-resolution spikes.
A unified AI platform, such as eesel AI, is designed to bring knowledge, automation, and AI together from the start. It offers instant knowledge connections, integrated testing, total control over automation, and seamless helpdesk integration within a single environment.
