GPTs vs Actions vs Plugins: A clear guide to OpenAI's evolving ecosystem

Keeping up with OpenAI’s new features can feel like you’ve finally figured something out, only for them to release a whole new thing the next day. One minute, you're getting the hang of plugins, and the next, they're being swapped out for something called "GPTs" that use "Actions." If you're trying to figure out how your business can actually use AI, this constant shuffle can be more confusing than helpful.

This article is here to cut through the noise. We'll break down the key terms in OpenAI's world: Plugins (what we used to have), custom GPTs (what we have now), and Actions (the engine that makes them work). We'll look at what they are, what they can do, and, more importantly, where they come up short for real-world business automation.

While these tools are a fascinating peek at what's to come, you'll see why a more focused, purpose-built approach is often what you really need to get things done.

A quick history of extending ChatGPT

To really get what’s happening now, it helps to see how we got here. Let's take a quick walk through the evolution from plugins to the current setup of custom GPTs and Actions.

Understanding ChatGPT plugins

Remember plugins? They were OpenAI's first real attempt to let ChatGPT talk to the outside world. Think of them as little add-ons you could grab from a dedicated store, each giving the standard ChatGPT a new skill. One plugin might let it search for flights on Kayak, while another could pull up-to-the-minute weather data.

Their real magic was that you could mix and match them. You could have up to three different plugins running in the same conversation. This was a pretty big deal. You could ask for flight options, check the weather at your destination, and look for restaurant reservations, all in one continuous chat. It felt like a true multi-tool.

But OpenAI decided to go in a different direction, and plugins are now being phased out for a new, more integrated system.

What are custom GPTs?

Custom GPTs are the official replacement for plugins. They are specialized versions of ChatGPT that anyone can build for a specific job, often without touching a single line of code. You could create a GPT that’s a friendly math tutor for your kids, a guide that knows all the rules to your favorite board game, or a helper that answers questions based on your company's private documents.

They're built on two main parts:

• Custom Instructions: This is basically a fixed set of directions you give the GPT to define its personality and role. For instance, you could tell it, "You are a helpful assistant for a software company. Always respond in a friendly and encouraging tone."

• Knowledge: You can upload your own files, like PDFs or spreadsheets, to give the GPT a specific knowledge base to pull answers from.

At the end of the day, a custom GPT is a specialized chatbot, tweaked to be an expert on one very specific topic.

What are GPT actions?

Actions are what give custom GPTs their real power. They are the piece that allows a GPT to connect with the outside world by calling external APIs. Put simply, Actions are the technical replacement for the old plugin system.

An "Action" isn't something you use on its own. It's a capability you build into a custom GPT to let it do things beyond just chatting. For example, you could create an Action that lets your GPT look up a customer's order status in your database, create a new ticket in your helpdesk, or add a lead to your CRM.

While the idea is solid, setting up Actions isn't as easy as uploading a PDF. It requires a bit of technical comfort, usually involving something called an OpenAPI specification that tells the GPT how to talk to your other tools.

Comparing capabilities

Now that we know the definitions, let's get practical. The switch from plugins to GPTs wasn't just a rebrand; it changed what's possible and introduced some new headaches.

The lost advantage: Combining multiple tools

The single best thing about the old plugin system was that different tools could work together. As plenty of users on the OpenAI community forums have pointed out, you could chain different plugins to handle complex requests.

OpenAI Community Forums

You could ask a travel plugin for the best flights to Miami and then, in the same chat, ask a weather plugin for the forecast. The AI understood the context and used both tools to give you a complete answer.

With custom GPTs, that ability is gone.

You can only use one custom GPT at a time. Each one is stuck in its own silo, unable to talk to or use the skills of another. If you have a "Weather GPT" and a "Travel GPT," you have to open two separate chats. This makes it impossible to tackle tasks that need information or actions from multiple systems, which is pretty much how every business workflow operates.

The power and pitfalls of a single-purpose design

The main strength of a custom GPT is its focus. If you upload a specific knowledge base, you can create a pretty effective "expert" on that one topic. For example, a GPT trained on your internal HR policies can do a decent job answering employee questions about benefits or time off.

The problem is, real business processes are almost never that simple. A customer support issue, for instance, is a whole journey, not a single question. It might involve:

1. Figuring out which department the ticket should go to.

2. Looking up the customer's order history in Shopify.

3. Checking their subscription status in your billing system.

4. Updating the ticket in Zendesk.

5. Drafting a personal reply based on all that gathered info.

A custom GPT can be trained to do one of those things, but it can't manage the entire flow. It’s good for fetching a single piece of information but falls flat when you need it to run a whole workflow. For that, you need a proper workflow engine. AI platforms like eesel AI are built to manage these complex sequences, like triaging tickets, pulling customer details, and drafting a thoughtful reply, all in one automated process.

A diagram showing a multi-step support automation workflow, which requires a purpose-built platform instead of a single-purpose GPT.

The user experience: From clunky to unreliable

Beyond the big-picture limitations, the actual experience of building with OpenAI's tools can be a bit of a letdown. There's a big difference between a cool tech demo and a business tool you can depend on.

The setup challenge: Is it really 'no-code'?

Creating a basic, knowledge-based GPT is genuinely simple. You chat with an interface, upload a few documents, and voilà, you have a specialized chatbot. It's impressive.

But the moment you want to add real-world functions with Actions, that "no-code" promise starts to crumble. You're suddenly expected to understand OpenAPI specifications, manage API key authentication, and figure out why a call failed without any real developer tools. As many builders on Hacker News have pointed out, the experience can be buggy, inconsistent, and confusing. The very tools meant to empower non-technical folks quickly become a major technical roadblock.

Missing pieces for business automation

When you try to use custom GPTs for serious business processes, you hit a wall pretty fast. They're missing a few key features that are essential for any kind of reliable automation:

• No Step-Chaining: Actions are just one-off calls. You can't create a dependable sequence where the result of one action feeds into the next one. This makes building any kind of multi-step workflow a fragile, error-prone exercise.

• Limited Memory: During a long conversation or a complex task, GPTs can forget earlier instructions or lose important context. This lack of reliability is a deal-breaker for any process that has to run the same way every single time.

• Lack of Native Integrations: Need to connect to a tool like Slack? You have to build a custom Action for every single function you need. This is a massive time sink compared to platforms that offer big libraries of pre-built, one-click connectors.

These gaps are exactly what purpose-built automation platforms are designed to fill. For example, eesel AI provides a fully customizable workflow engine with native, one-click integrations for helpdesks, chat tools, and knowledge sources. You can easily build, test, and launch multi-step AI agents using its simulation mode, making sure everything works perfectly before it ever talks to a customer.

The eesel AI platform showing a simulation mode, a key feature for reliable business automation that isn't available when comparing GPTs vs Actions vs Plugins.

Understanding cost and security

For any business, the conversation about new tech eventually boils down to two things: how much does it cost, and is it secure?

The pricing model

To use or create custom GPTs, you need a paid ChatGPT subscription; they aren't available on the free plan. Here’s a quick look at the official pricing:

Data privacy concerns

This is a big one. If you're using an individual ChatGPT Plus plan, your conversations and any files you upload can be used to train OpenAI's models unless you dig into the settings and opt out.

While the Business and Enterprise plans offer stronger privacy protection by default, many companies start experimenting on a Plus account, unknowingly putting sensitive information at risk. For a business, data privacy can't be an afterthought or an upgrade.

It has to be standard. Platforms like eesel AI are built with enterprise security in mind from the ground up. Your data is never used to train generalized models, it's encrypted everywhere, and can be hosted in the EU on request, ensuring your knowledge always stays yours.

GPTs vs Actions vs Plugins: Choosing the right tool for real automation

So, what's the final verdict on GPTs vs Actions vs Plugins?

• Plugins were a pioneering but messy first step toward a more capable AI.

• Custom GPTs are a fantastic tool for creating personalized chatbots and knowledge experts for yourself or a small team.

• Actions give GPTs their real muscle, but they're still too technical and unpredictable for building business processes you can count on.

For true business automation, especially in critical areas like customer support, you need more than a generic, single-purpose tool. The future belongs to integrated AI agent platforms that are designed from day one to handle the complexity, reliability, and security that businesses need.

Build a true AI support agent in minutes

Tired of the limitations, technical hurdles, and privacy worries that come with building on ChatGPT? eesel AI offers a truly self-serve platform to build and deploy powerful AI agents for customer support. You can unify all your knowledge, automate entire workflows, and go live in minutes, not months. Try it for free today.