OpenAI’s Agent Builder arrived with big promises
The idea is ambitious: a tool capable of creating AI agents visually, simply, and fully integrated into the OpenAI ecosystem. For a lot of people, the first thought was immediate — is this going to replace Zapier, Make, and n8n?
These platforms have dominated the automation market for years, and any new development with OpenAI’s name on top already draws more than enough attention. But before jumping to any conclusions, there’s a detail that flew under the radar in most analyses.
One user decided to get hands-on. He tested the tool called AgentKit, created an agent from scratch named Content Ideation, and when it came time to run it, he ran into something totally unexpected.
It wasn’t a bug. It wasn’t server instability. It was a biometric verification requirement just to run the agent he had literally just built. 😮
That single issue completely changes the conversation about the tool’s real potential — and about what it means for the user experience of anyone looking to explore AI automation today.
What the Agent Builder is and why so many people got excited
The Agent Builder is an interface within OpenAI’s platform that lets you assemble AI agents in a much more structured way than simply chatting with ChatGPT. The idea is to give users control over the agent’s behavior, the tools it can use, the data sources it accesses, and even the sequence of actions it performs autonomously. All of this in a visual environment, without needing to write a single line of code to get started. For anyone working in automation, productivity, or digital product development, that sounds like music to the ears.
The excitement makes sense when you remember that OpenAI is the company behind the most well-known language model in the world. Integrating GPT’s capabilities directly into an agent builder with native integrations is a significant step. It means you no longer need to rely on poorly documented external APIs or middleman platforms just to get a language model to connect with other tools. The promise is that everything stays within the same ecosystem, with less friction and more control.
But promises and delivery are two different things, and that’s exactly where one user’s real-world experience raised an important red flag.
The two problems that show up the moment you try to test your agent
The test conducted with AgentKit revealed two obstacles that pop up right after you create an agent. These aren’t minor details — they’re barriers that completely block you from using the tool until they’re resolved.
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The first problem is that you simply cannot run the agent. After all the configuration, behavior setup, and tool selection, the run button doesn’t work. The system blocks the action and requires a prior step that wasn’t clear during the creation process.
The second problem is that you can’t even preview the agent before publishing it. That means there’s no way to test its behavior, tweak details, or verify if everything is working as expected. To do anything — run or preview — you need to complete the Organization Verification step.
When you click to verify, you’re directed to a dedicated page for the verification process. And here comes the surprise: after starting the procedure, a pop-up appears asking you to begin an identity check — the now-infamous Start ID Check. This is the moment the platform asks for something few people expected to find in an automation tool: sharing your biometric data.
Yes, you read that right. To test an AI agent that you built yourself, OpenAI requests personal biometric information. And this is where the scenario starts looking very different from what competing platforms offer.
Biometric verification: what’s behind this requirement
The biometric verification OpenAI requests when you try to activate or run an agent isn’t some random technical quirk. It’s part of a broader company policy related to user identity and the responsible use of advanced AI capabilities. OpenAI has been investing in identity verification mechanisms precisely because autonomous agents have a much higher level of access and execution capability than a simple chatbot.
An agent can send emails, access files, make requests to external systems, and make decisions without real-time human intervention. That requires a different level of accountability than just generating text in a chat window.
The collection of biometric information in this context typically involves facial recognition or another type of unique physical data from the user, processed by a third-party identity verification service. This model is already used by fintechs, credit platforms, and digital financial services — but it’s still new territory in the world of AI productivity tools.
For many users, especially developers and content creators who are used to fast testing and prototyping workflows, this step feels disproportionate. It’s like you carefully built something and, right when you’re about to hit play, someone asks for your government ID and a selfie. And as the original tester himself reported: he’s completely against sharing his personal data just to use an AI agent.
From OpenAI’s perspective, the logic is defensible. AI agents operate with broad permissions, and if misused, they can cause real damage — whether through mass spam, unauthorized data access, or automated actions at scale. Requiring biometric data is a way to ensure there’s a real, identifiable person behind every active agent on the platform. The problem is that this decision comes with a direct cost to the user experience, especially for people in the exploratory phase who just want to understand what the tool can do before making any serious commitment.
Why Zapier, Make, and n8n aren’t going anywhere anytime soon
This is where the conversation gets really interesting. Despite all the hype around OpenAI’s Agent Builder, there are concrete, practical reasons why established automation platforms are holding strong — and will probably continue to do so for quite a while.
The original tester highlighted three key points, and each one is worth expanding on:
Strong and diverse integrations
Zapier, Make, and n8n have hundreds — and in some cases thousands — of integrations with tools and services that businesses already use every day. We’re talking about connections with Google Workspace, Slack, Notion, Airtable, HubSpot, Stripe, Shopify, WordPress, GitHub, and way more. These integrations were built over years, battle-tested in production by millions of users, and refined based on real feedback. Replicating that ecosystem from scratch isn’t trivial, and OpenAI’s Agent Builder is still far from offering that kind of breadth.
Ease of use for non-technical people
Platforms like Make and Zapier were designed from day one to be accessible to people without a technical background. The visual interface, ready-made templates, built-in tutorials, and drag-and-drop logic make automation accessible to marketing, sales, operations, and management professionals who don’t know how to write code. The original tester made a point of mentioning that he comes from a non-technical background, and he can still use these tools effectively. That accessibility is a massive competitive advantage that the Agent Builder hasn’t yet shown it can match.
The ability to run locally with n8n
n8n has an extra ace up its sleeve that especially appeals to developers and teams concerned about privacy and data control: its basic version can be run locally or hosted on your own infrastructure, outside of n8n’s official site. This means your data, your automation workflows, and your agents stay under your direct control, without depending on third-party servers and without needing to share any personal or biometric information to get started. In a scenario where OpenAI requires biometric verification to run an agent, this advantage becomes even more relevant.
The real impact on user experience and tool adoption
When the user experience gets interrupted by an unexpected barrier during the very first interactions with a tool, the psychological effect is immediate. The person who was excited about the Agent Builder’s possibilities starts questioning whether it’s worth the effort. This is especially true in a market where alternatives like Make, n8n, and Zapier let you start automating within minutes, without providing any kind of biometric data.
The onboarding experience is one of the most decisive factors in the adoption of any digital product. A verification barrier right out of the gate can be enough to push away a significant portion of potential users. And we’re not talking theory here — the test report shows exactly this happening in practice.
The impact goes beyond the sign-up moment. The integrations available in the Agent Builder are one of its biggest draws — the ability to connect your agent with external tools, databases, APIs, and popular services. But if a user can’t even get to the stage of setting up those integrations because of the verification barrier, all that potential stays locked away. It’s like opening the packaging on a sophisticated tech product only to find a padlock before you can even turn it on.
The most sensitive point here is the contrast between what was promised and what was delivered in the initial experience. OpenAI’s messaging around the Agent Builder emphasizes simplicity, autonomy, and the ability to create powerful agents in an accessible way. But requiring biometric information right at the start introduces a layer of complexity and discomfort that was never part of the official narrative. 🤔
Privacy and personal data: a legitimate concern
The privacy question is no minor detail in this story. At a time when regulations like Brazil’s LGPD and Europe’s GDPR reinforce the importance of personal data protection, requesting biometric information to activate a productivity feature is a move that deserves attention.
Biometric data is classified as sensitive personal data under Brazilian law — and similarly under GDPR in Europe and various state-level privacy laws in the US. This means processing such information requires additional safeguards, clear justifications, and often explicit consent from the data subject. For a user who just wants to test an AI agent before deciding if the tool is worth it, providing that kind of data can feel — and often is — excessive.
Competing platforms like Zapier, Make, and n8n don’t require anything like that. You create an account with an email, pick a plan, and start using it. Done. This difference in onboarding and verification approach might seem subtle, but in the productivity tools market, every extra step in the activation process is an opportunity to lose a user to the competition.
What to expect from the Agent Builder going forward
Despite the initial friction with the verification step, the Agent Builder still has a lot to offer — and it would be premature to write it off based solely on this onboarding experience. OpenAI has a track record of adjusting its products quickly based on community feedback, and the complaint about the biometric barrier is already circulating in forums, social media, and technical communities with enough momentum to reach the right ears.
It’s reasonable to expect that in upcoming versions, the verification process will be refined. Maybe it’ll become optional for certain use cases or get replaced by less invasive methods for users who already have a verified account on the platform.
Integrations are the point that excites the technical community the most, and for good reason. The idea of having an AI agent that doesn’t just chat but actually acts — fetching real-time information, filling out forms, sending notifications, triggering other systems — is exactly the kind of automation that tech, marketing, and operations professionals are looking for. If OpenAI can deliver that set of integrations reliably and with a smoother experience than the current one, the Agent Builder has everything it needs to become a benchmark in the space.
For now, it’s a wait-and-see situation. The tool exists, it has real potential, and it’s growing in capabilities. But the experience of someone who tried to use it and hit the biometric data wall serves as an important reminder: powerful technology needs an equally well-crafted experience to become truly relevant.
The technical product might be excellent, but if the user journey stumbles right at the beginning, the impact stays bottled up — and no amount of advanced features can fix that problem as long as the front door remains an obstacle. 🚀
