> For the complete documentation index, see [llms.txt](https://dappos.gitbook.io/docs/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://dappos.gitbook.io/docs/dappos/xbubble/bubble-code/why-current-coding-agents-are-still-difficult-for-non-technical-users.md).

# Why Current Coding Agents Are Still Difficult for Non-Technical Users

Today's coding agents are powerful, but they still assume a certain level of technical understanding.

For developers, this is not a problem. A developer can read generated code, inspect errors, decide which architecture to use, configure services, test the output, and deploy the application.

For non-technical users, the experience is different.

**Technical Setup**

Building and launching a software project often involves domain names, servers, hosting, databases, third-party APIs, deployment environments, configuration files, and service accounts. Even if AI provides instructions, users still need to understand what each part means and how to make the right choice.

**Technical Decision-Making**

A coding agent may ask questions such as: Do you need an admin panel? How should order status be managed? What user roles are required? What happens when a form submission fails? How should data be stored?

These questions are normal for developers, but difficult for users without a software background.

**Product Requirements**

A business owner may say, "I want to create an online store," but that simple sentence includes many hidden steps: product pages, product images, descriptions, customer information, order state management, fulfillment status, backend access, and future edits.

The problem is not only whether AI can write code. The problem is whether ordinary users can turn a business idea into a clear, executable software workflow.

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