Using vibe coding to scratch an itch has many of the same issues as using low-code and no-code development tools. That’s not surprising. Putting together something quickly to solve a specific problem lacks the level of discipline of software engineering, introducing inefficiencies that come from not following architectural guidelines and policies.
I recently reviewed the AI-generated code I used to quickly build a PDF archive of some HTML-formatted content. The program used common libraries in unusual ways, often combining three or more libraries when all the necessary calls were in one, with a program flow that was hard to understand in places. The result was at best unmaintainable. The only way it was acceptable was that I’d thrown it together in an hour to solve a specific problem, and once I’d built that local PDF archive I could simply throw the program away.
But what if your sloppy AI-generated code was needed by an entire department, or had become part of an essential business process? The risk is that something, somewhere, would inevitably change, and that essential business process would fail, with no practical way of fixing the code. The risk only compounds when the author has left their role and there is no one left who remembers or understands the initial prompts that produced the code.
Adding AI-powered coding tools to products and platforms is risky. But they are too useful to ignore. Like low-code tools, AI tools put coding in the hands of the people who understand the business problems and helps them automate processes on their own schedule.
What’s needed is a way to improve the harnesses used by the coding agents, taking concepts from specification-driven development and using them to build guardrails and standards into the code they produce, with the aim of building code that’s maintainable.
Introducing Rayfin Fabric Apps
Microsoft Fabric is intended to be where all your businesses operational data lives, offering a way to query and explore data at scale. It builds on a mix of big data and data warehousing concepts, using open formats for storage and data mirroring tools to bring data from existing applications into its data lakehouses, as well as supporting streamed data.
Microsoft Fabric allows you to build and deploy your own agents inside the platform to work with your data. However, in many cases, you will need a more focused approach to exploring and using defined subsets of data. The recent addition of Fabric Apps to the platform allows business analysts to deliver applications that use data in Microsoft Fabric to provide essential business insights.
Fabric Apps are built on top of Rayfin, a new TypeScript-based SDK that is designed to work with both humans, via CLI, and AI agents. A partnership with Replit allows Microsoft Fabric users to build apps using Rayfin and AI assistance in Replit and deploy them directly into Microsoft Fabric. Here Replit support is best thought of as a tool for business users, whereas developers can use tools like the GitHub Copilot CLI and Visual Studio Code, which let you mix traditional and AI-supported development processes.
The intent is to provide the tooling that business users need to create data-powered applications inside Fabric, making application development part of the process of exploring data. Rayfin apps can be connected to raw data or draw on semantic models built with tools like Power BI, using DAX for analytics rather than SQL or KQL queries.
Data app development tools for coders and non-coders
The key to this approach is the open-source Rayfin SDK, which offers a way to build out the necessary definitions for data models that can then be used by agent-based tooling. The SDK allows you to set up access to specific models, along with the necessary permissions, before users begin to build on them. Rayfin turns those models into GraphQL endpoints, with TypeScript clients sitting between them and the data stored in Microsoft Fabric — adding an extra layer of protection as the APIs are automatically type safe.
Building a Rayfin-powered Fabric App starts with using Rayfin to define a data structure with decorators that apply types to the data you want to use in your application and defining each table as a class. For example, a table of employee names and contact details would be exposed as an entity that contains an employee class, with decorators that create ID number, name, and address strings, all using structures held in the @microsoft/rayfin-core definitions. Other options allow you to begin to define access controls around the data, so that it can be used only by authenticated users with the correct group privileges.
This is the most important part of any Rayfin-based application development, as it’s here that you’re setting up the guardrails for ongoing user-led development. You’re not limited to one basic application scaffold, either, as there are a series of different
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