Remember the buzz when OpenAI launched custom GPTs? The excitement was real. We were all imagining building our own little AI assistants, perfectly tuned for a specific job or with a unique personality. The core of that dream was a feature called GPT Actions, which was supposed to let these assistants connect to our everyday apps and actually get stuff done.
But if you were one of the many who tried to build one, you probably found the whole thing more frustrating than futuristic. The promise was a simple, powerful future, but the reality was a tangle of technical hurdles. So, what was the real difference between GPTs vs Actions, why did this brilliant idea fall flat for businesses, and what does the next generation of AI automation really look like?
The GPTs vs Actions difference: What are custom GPTs and Actions?
To really get what went wrong, we first need to get on the same page about what each component was meant to be. Let’s break down the two key parts of the equation.
What is a custom GPT?
A custom GPT is essentially a personalized version of ChatGPT. You can give it a specific set of instructions, a particular personality (say, a friendly and helpful support agent or a witty creative partner), and even a dedicated knowledge base by uploading files like PDFs or internal documents.
Think of it like this: if the standard ChatGPT is a generalist librarian who knows a little bit about everything, a custom GPT is a specialist who’s an absolute expert on one niche topic. Maybe that’s 19th-century literature, or maybe it’s your company’s internal HR policies. This feature, which is available on paid ChatGPT plans, allows you to create these focused assistants for specific tasks. You could have one for brainstorming marketing copy in your brand’s voice and another for summarizing dense, technical research papers. It’s a great way to mold the AI to your specific needs.
What were GPT Actions?
GPT Actions were the other half of the puzzle, and this is where things started to get complicated. This now-deprecated feature was designed to let a custom GPT connect with external apps and services using APIs. This was the key that was supposed to turn your specialized GPT from a conversationalist into a doer. An Action was what allowed a GPT to actually perform tasks in the real world.
For example, with the right Action, your GPT could theoretically:
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Check your Google Calendar to see if you’re free for a meeting.
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Create a new customer support ticket in a help desk like Zendesk.
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Pull live inventory numbers from a company database.
So, the custom GPT was the AI’s “brain” and personality, while the Action was its set of “hands,” letting it interact with the world outside the chat window. The whole GPTs vs Actions discussion was really about how this brain and these hands were supposed to work together to automate tasks.
The promise of the GPTs vs Actions model: A world of connected automation
The idea behind GPT Actions was genuinely exciting. It painted a picture of a future where you could use plain, everyday language to kick off complex workflows, all without ever leaving your chat interface. Developers and businesses were buzzing with ideas for how this could fundamentally change how we work.
A few of the use cases that got everyone excited were:
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For sales teams: "Find the Head of Marketing at Acme Corp on LinkedIn, get their contact info, and draft a friendly follow-up email based on our last conversation."
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For support agents: "A customer wants a refund. Look up their order number in Shopify, confirm it's eligible, and process the return."
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For internal operations: "Book a 30-minute meeting room for the engineering team sometime tomorrow afternoon and send out a calendar invite."
This was meant to be the bridge connecting conversational AI with practical, everyday business automation. It promised to make powerful tools accessible to anyone who could type a sentence. It was a fantastic idea, but as many of us found out, the execution was another story.
The reality of GPTs vs Actions: Common pitfalls
While the vision was grand, trying to actually use GPT Actions for real-world business tasks revealed a lot of cracks. The system was plagued with challenges that prevented it from ever gaining serious traction, especially for businesses that needed something reliable and easy to manage.
The high technical barrier to entry
Anyone who spent time scrolling through developer forums or Reddit threads back then can tell you that building a working Action was anything but a "no-code" experience. The technical hurdles were steep.
First, you had to write a detailed OpenAPI schema. This is basically a highly structured document, written in JSON or YAML, that acts as a translator, telling the GPT how to talk to an external tool's API. This step alone required a pretty good understanding of API design principles, which immediately put it out of reach for most marketing, sales, or support professionals.
Then you had to tackle authentication. Setting up a secure connection using API keys or OAuth is complex, and for many businesses, the idea of handing over sensitive credentials to a system with so little transparency was a non-starter. Even for experienced developers, the process often turned into a headache of trial and error. You'd spend hours tweaking the schema, trying to figure out why the GPT wasn't understanding a specific command or why an API call was failing. It just wasn't the plug-and-play solution we were all hoping for.
A fragmented and limited user experience
One of the biggest workflow killers was that an Action was completely siloed within its specific custom GPT. You couldn't just be having a regular conversation with GPT-4 and call upon your special "Zendesk Assistant" to perform a quick task.
This meant you were constantly forced to switch between different chat windows, which completely broke your flow of work. Imagine you’re analyzing a customer feedback report in one chat, and you have an idea for a new help article. You'd have to copy your insights, open a different custom GPT built for your knowledge base, paste everything in, and then give it the command. It was clunky, inefficient, and felt like a step backward.
Worse yet, you couldn't chain actions together to create any kind of meaningful, multi-step workflow. Each action was a one-off event. The GPT had no built-in memory or logic to handle a real process like, "Find a new lead, enrich their contact data with a second tool, then add them to our CRM." It could only do one isolated piece at a time, which severely limited its use for any kind of serious business automation.
Unreliable performance: A major issue
For businesses that were hoping to use GPTs in customer-facing roles, the platform simply wasn't built for it.
There was no official store or easy way for users to discover and install custom GPTs, so all the effort you poured into building one was unlikely to reach an audience. More importantly, there was no way to simulate or test how your Action would perform with real-world inputs before you set it live. You basically had to build it, release it, and hope for the best. For any serious business, deploying untested automation in a live environment is a massive risk.
Finally, the lack of control was a major issue. You couldn't set granular rules to define when the AI should act and when it should stay quiet. A support team might only want to automate responses to simple "where is my order?" questions, but not complex technical issues. With GPT Actions, that level of control just wasn't possible, and that made it a dealbreaker for teams that need to maintain quality and trust.
The evolution beyond GPTs vs Actions: From clunky Actions to integrated AI agents
The struggles with GPT Actions ultimately taught the industry a valuable lesson: businesses don't need a complicated DIY toolkit. They need a fully integrated, reliable, and user-friendly platform that delivers on the promise of automation without the headaches. This is where AI agent platforms like eesel AI come in, picking up where GPT Actions left off.
Go live in minutes
Instead of spending weeks wrestling with OpenAPI schemas, modern AI agent platforms offer one-click integrations with the tools you already rely on, like Zendesk, Freshdesk, and Intercom.
The entire setup process is designed to be truly self-serve. You can securely connect your knowledge sources, whether that's past support tickets, internal wikis in Confluence, or procedural guides in Google Docs, and have a functional AI agent up and running in minutes. There's no mandatory sales call or waiting for a developer to help you. This approach completely removes the technical barrier that stopped so many people from ever getting started with GPT Actions.
Modern AI agent platforms allow for one-click integrations to connect all your knowledge sources, a key advantage in the GPTs vs Actions comparison.Gaining total control
Where GPT Actions were isolated and rigid, a platform like eesel AI gives you a complete workflow engine. You can build out precise rules to selectively automate only the types of queries you're comfortable with, giving you total command over the entire process.
You can also define custom actions that empower the AI to do much more than just answer a question. It can look up real-time order information from your backend, escalate a ticket to the right human agent, or automatically update ticket fields with relevant tags. This is the kind of granular control that was always missing from custom GPTs, allowing you to build trust in your automation and scale it with confidence.
Dedicated AI platforms provide granular control and custom rules, a necessary evolution from the rigid model of GPTs vs Actions.Test with confidence
One of the biggest risks with GPT Actions was going in blind, with no real way to test your creation. eesel AI solves this problem with a powerful simulation mode.
Before your AI agent ever interacts with a live customer, you can run it on thousands of your own historical support tickets. The platform will show you exactly how the AI would have responded in each case, giving you an accurate forecast of its performance and resolution rate. This risk-free testing environment allows you to fine-tune the AI's behavior, prove its value to your team with hard data, and roll it out knowing exactly what to expect.
The ability to test and simulate performance on historical data is a key advantage for AI agent platforms over the original GPTs vs Actions model.ChatGPT pricing
Just for context, creating and using custom GPTs requires a paid plan from OpenAI. Here’s a quick rundown of the pricing for their individual and team plans.
| Plan | Price (per user/month) | Key Features for Building |
|---|---|---|
| Plus | $20 | Access to GPT-4o, create & share GPTs for personal use. |
| Team | $25 (annual billing) / $30 (monthly) | Everything in Plus, dedicated workspace, create & share GPTs with your team. |
| Enterprise | Contact Sales | Everything in Team, advanced security, unlimited GPT-4o access, customization. |
Pricing was sourced from OpenAI's official pricing page in late 2024. Keep in mind that plans and features can change.
Automation requires more than a chat window
At the end of the day, custom GPTs are a fantastic tool for personalizing a chat experience or for individual productivity hacks. But the great GPTs vs Actions experiment showed us that serious business automation needs more than a clever feature bolted onto a consumer chat app. The original implementation was too complex for most users, too fragmented to fit into real workflows, and too unreliable for customer-facing tasks.
True automation demands a platform that was built for the job from the ground up. The future isn't about tinkering with clunky connections in a chat window; it's about deploying smart, integrated AI agents that are easy to set up, safe to test, and powerful enough to handle real business processes from start to finish. For teams that are ready to move past the hype and start getting real results from AI, a dedicated platform is the clear path forward.
Ready to see what a true AI agent can do for your support team? Try eesel AI for free and deploy a fully functional AI assistant in minutes, not months.
Frequently asked questions
The initial vision for GPTs vs Actions faced significant challenges due to high technical barriers, a fragmented user experience, and unreliable performance. Businesses found it too complex and rigid for real-world, scalable automation needs.
A custom GPT was designed to be the AI's "brain" or personalized personality and knowledge base. Actions were the "hands," allowing the GPT to connect with external applications and perform tasks in the real world via APIs.
Key technical hurdles included the necessity of writing detailed OpenAPI schemas, which required significant API design knowledge. Complex authentication setup and a frustrating trial-and-error debugging process also posed major barriers.
The siloed nature meant users had to constantly switch between different chat windows, breaking their workflow. Additionally, the system lacked the ability to chain actions together for multi-step processes, limiting its utility for complex automation.
The next step involves dedicated AI agent platforms that offer one-click integrations, full workflow engines, and robust testing environments. These platforms provide greater control, reliability, and ease of use compared to the original GPTs vs Actions model.
Yes, custom GPTs remain a valuable tool for personalizing chat experiences, providing specific instructions, and leveraging dedicated knowledge bases for individual productivity. They excel at tailoring the AI's conversational abilities to niche topics or roles.
