OpenAI just unveiled AgentKit—a no-code platform that lets anyone build AI agents. In theory, you, a non-technical association professional with zero coding experience, can now build an intelligent system to automate your content curation, handle member inquiries, or streamline event registration. All in under five minutes. Just describe what you want, and the AI builds it for you.
In practice? Well, it's not quite that simple. And before you invest time and organizational resources into building on any platforms like these, there are some realities about agent builders that every association leader should understand.
Let's start with what OpenAI's Agent Builder actually delivers. Yes, it's marketed as a no-code solution. Yes, you can describe what you want in natural language and watch it create workflows. But here's what the demos don't always show: you still need some basic understanding of how automated workflows function.
Think about concepts like schemas, inputs and outputs, data flow between different systems. The interface is visual rather than text-based code, but it's not quite the point-and-click simplicity you might expect. I tested the builder myself and found it wasn't nearly as intuitive as established workflow platforms like Zapier. The learning curve exists, even if it's gentler than traditional coding.
That said, there's genuine value in experimenting with these tools. Even if you don't end up using OpenAI's platform for production, playing with agent builders helps non-technical people develop workflow literacy. You start to understand how data moves through systems, what triggers actions, and how AI can fit into your existing processes. Just manage your expectations going in—it's still early-stage technology with rough edges.
When you use OpenAI's Agent Builder, you're not just selecting a tool. You're buying into what's called a "full stack solution." That means everything—the AI models, the infrastructure they run on, the APIs that connect them, the agent builder itself, and even the user interface—all comes from one vendor.
This approach has genuine benefits. It works. It's integrated. For many associations, it makes complete sense. If you're a small organization with limited IT resources and a lower tolerance for technical complexity, having everything in one place can be appealing. It's the modern equivalent of the old Microsoft days when everything ran on Windows, used Office, and lived on Exchange servers.
The question isn't whether integrated stacks are good or bad. The question is whether that level of integration is right for your organization's specific situation and future needs.
Here's where things get more complicated. When you build agents on a platform like OpenAI's AgentKit, you're not just using their tools—you're encoding your business logic into their environment. The workflows you create, the way you've structured your data, the connections between your systems—all of that lives in their platform.
Swapping out AI models is relatively straightforward. If you build your software reasonably well, you can switch from one language model to another without too much difficulty. But switching platforms? That's a different story entirely. Once you've built your business logic into a proprietary agent builder, moving that to another platform takes real effort. We're talking potentially six months or more of work to migrate everything over.
This matters because of how your data is handled. Your proprietary content—member information, research, educational materials—lives in their cloud environment, governed by their security protocols and, critically, their terms of service.
AI companies change their terms and conditions frequently, and they seem to feel entitled to do so without much notice.
Take Anthropic, the company behind Claude, as a recent example. They've built their brand on being privacy-forward, focused on AI safety and alignment. They were widely seen as one of the "good actors" in the AI space. Then recently, they announced they would start training future models on user data by default—the complete opposite of their previous stance. The community was shocked.
If even the companies positioning themselves as privacy-conscious can make these kinds of sudden reversals, what does that mean for your association's data when you're fully dependent on their platform?
The frequency of these changes is concerning. OpenAI has adjusted its policies multiple times. Google has updated its approach to training data. Microsoft has modified its Azure OpenAI terms. Each time, organizations using these platforms face a choice: accept the new terms or begin the lengthy migration process.
When you're locked into a single platform—when your business logic, your workflows, and your data architecture all depend on one vendor—you lose negotiating power. You can't easily walk away. And these companies know it.
This isn't about OpenAI specifically being problematic. It's about the power dynamic that exists when you're completely dependent on any single vendor. OpenAI has done remarkable work and leads the consumer AI space with over a billion monthly active users. But the AI landscape changes too quickly to bet your entire infrastructure on one company.
The perspective from practitioners who've worked in technology for decades is consistent: it's not about being anti-any particular vendor. It's about maintaining optionality. When a vendor controls your entire stack—from the models you use to the infrastructure they run on to the tools you build with—they have significant leverage over your organization.
What works best today may not be what you need in six months. New model architectures emerge. Different providers develop faster inference capabilities or better pricing. More sophisticated security options become available. If you're locked into a single platform, you can't take advantage of those innovations without a major migration project.
Here's the smart approach: use integrated platforms like OpenAI's Agent Builder for prototyping and learning. Build quick proofs of concept. Test whether an agent-based approach solves your problem. Learn how these systems work without committing significant resources.
But make that distinction clear from the start. Is this a proof-of-concept, or is this your permanent architecture?
Some associations will decide the integrated stack is right for them even in production. That's a legitimate choice, particularly if:
But even if those conditions apply today, think about where your organization is heading. As you grow, as your data becomes more valuable, as your technical sophistication increases, will this platform still serve you well?
This is fundamentally a business decision, not just a technical one. Your IT person—if you have one—can provide input on capabilities and integration challenges. But the strategic questions about vendor dependency, data sovereignty, and future flexibility need to be answered by organizational leadership.
The good news is that OpenAI's Agent Builder isn't your only option for building AI workflows. Platforms like Zapier, Make, and N8N offer workflow automation that isn't locked to a single AI provider. These aren't enterprise solutions, but they're excellent tools for less technical people who want to experiment with agentic workflows without committing to a single AI vendor.
Zapier, for instance, has been in the workflow automation space for years and offers AI integrations without requiring you to commit entirely to one ecosystem. Make (formerly Integromat) provides similar functionality with more complex workflow capabilities. N8N is an open-source alternative that gives you even more control over your infrastructure.
The benefit of these model-agnostic platforms is straightforward: as the AI market evolves, you can evolve with it. When a new model provider offers better performance or pricing, you can switch without rebuilding your entire system. When security requirements change, you have options for how to address them.
These platforms still require some basic workflow literacy, but they offer something valuable in return: independence from any single AI vendor's decisions about pricing, privacy, or platform direction. They're ideal for prototyping and learning, and they keep your options open as you figure out what your long-term architecture should look like.
Before committing to any agent platform, work through these questions as a team:
Data Sensitivity: How much proprietary data will your agents work with? Member information, research data, and educational content all carry different risk profiles. The more sensitive your data, the more carefully you should evaluate where it lives and who has access.
Dependency Tolerance: How comfortable is your organization with being dependent on a single vendor? Some associations prefer the simplicity of one relationship. Others prioritize having multiple options. Neither approach is wrong, but you need to align on this as a leadership team.
Technical Resources: What IT resources do you actually have available? A small association with one part-time IT contractor has different considerations than a larger organization with a full technology team. Be honest about your capacity to manage more complex, multi-vendor architectures.
Timeline and Scope: Are you building a quick prototype to test an idea, or deploying a system that needs to work reliably for years? The answer should influence your platform choice significantly.
Future Flexibility: Even if your needs are simple today, where is your organization heading? If you're growing, if your data is becoming more valuable, if you're expanding your technology capabilities, choose a platform that can grow with you.
The emergence of no-code agent builders represents a genuine opportunity for associations. These tools make AI automation accessible to organizations that couldn't previously deploy it. That's legitimately exciting and worth exploring.
Just go into it with clear eyes about what you're choosing. The best prototyping tool isn't always the best production platform. An integrated stack that makes experimentation easy may create challenges down the road when your needs evolve or when vendor terms change in ways you didn't anticipate.
Test these platforms. Learn from them. Build your understanding of how AI agents can serve your members and streamline your operations. But as you move from experimentation to production, think carefully about the long-term implications of your architectural choices.
Your future self—the one dealing with a vendor relationship that's no longer serving you well, or trying to migrate years of business logic to a new platform—will thank you for thinking this through now rather than later.
Sidecar is releasing an agent selection toolkit in the coming weeks specifically designed to help associations navigate these decisions. It'll walk you through the key considerations, provide frameworks for evaluating platforms, and help you make choices that align with your organization's unique needs and constraints. Keep an eye out for that resource—it's designed to make this exact decision process clearer and more manageable.