Why AppGen is the future of low-code

It never quite bridged that chasm. It helped some: citizen developers, those who couldn’t code but wanted to build, gained the ability to build simple apps to solve low-hanging fruit. But the platforms forced a strict trade-off: simplicity or flexibility, not both. When the work demanded more than the visual builder could handle, users hit a wall.

That tension defined the category for a decade. Now it’s dissolving. Over the past 18 months, the lines between low-code application platforms, AI code generation tools, and agentic automation have begun to blur. Suddenly, there is a real opportunity for anyone to build anything. But within this new promise, the hard part remains the same: building tools that run against production data, under real governance, at enterprise scale.

Here’s how the market got here, and what it means for how software gets built.

AppGen has become industry shorthand for application generation, but there’s some considerable nuance:

Appgen (lowercase) is the underlying technical capability. It is the ability to generate functional apps from natural language. A user will “prompt” an AI using plain language, and the AI will generate code, components, and interfaces.

AppGen (CamelCase) is an umbrella market category coined by Forrester, defined as any platform that leverages AI to generate entire apps. These apps have entire user interfaces, logic, data schemas, and workflows. It collapses the traditional systems development lifecycle (SDLC) by combining the promise of low-code with the creativity of generative AI. The category spans:

Finally, we have a subset of AppGen, the enterprise AppGen platform. This is AppGen specifically for governed, production-grade internal apps and automations at scale. It combines appgen capabilities with other development modes, deep integrations to existing enterprise data systems, centralized governance, and multi-app ecosystem management. Retool is an enterprise AppGen platform.

The thread connecting these is the generation of the entire application. This is distinct from codegen, which is the generation of code through AI models. Codegen produces code snippets and components, not complete apps. But code is just a means to an end; what needs to be built—and shipped——and shipped—is apps.

For years, low-code application platforms (LCAP) had the “easy app development” market to themselves. Incumbents built visual development environments, won enterprise deals, and defined the category. The competition was between low-code vendors, not between low-code and something else. The problem with expansion was that these platforms were optimized for citizen developers, not professional engineers. They didn't care about how professional developers wanted to work, and the artificially low ceilings meant serious engineers largely avoided them. For pro devs, LCAP is coding with crayons.

But the entire concept of “easy app development” is undergoing the mother of all earthquakes. Two entrants have caused this shift:

Both began outside the enterprise, but neither is staying there and are on a collision course with the LCAP incumbents. The convergence is real. So is the gap between what these tools promise and what production systems require.

First, AI-only builders still lack enterprise fundamentals. Prototypes don’t run payroll or process insurance claims. These platforms are optimized for prototyping and individual developers, not production systems. They lack governance, auditability, secure internal data access, and operational controls. Infrastructure is owned, run, and secured by the vendor, not the customer. When a team builds something useful and wants to run it against production data, they discover the gap between demo and deployment.

Second, incumbents are adding AI, but generating brittle abstractions. Most major low-code vendors have released natural-language app generation features. The problem is that these platforms spent years optimizing for simplicity over flexibility, and the decisions they made on behalf of builders created abstraction layers that are now brittle. When AI tries to generate apps on top of those rigid, proprietary serialization formats, the results are limited. True developers still have constrained creativity, constrained expression, and the same artificially low ceiling carried forward into the AI era.

The category is collapsing. But the gap between ‘build me an app’ and ‘run this in production’ remains. That's the space enterprise AppGen fills.

While appgen can describe any tool that generates apps from prompts, an enterprise AppGen platform is designed to build, run, and govern production apps inside organizations. That means supporting the full software development lifecycle, combining multiple development modes (natural language, visual canvas, code), and baking in the governance, guardrails, and deployment flexibility that mid-to-large companies require.

Enterprise apps need trusted, secure access to the entire data stack. That means codified rules for how anyone should be able to access resources such as Salesforce, Databricks, or internal databases, whether they live in a VPC or elsewhere. Permissions, access controls, and data governance need to be set up once and universally enforced.

AI-only app builders fall short here. They don’t offer secure connections to internal databases or the ability to codify enterprise data access rules. AI code generation tools are neutral; they don't provide data infrastructure at all. Low-code incumbents generally handle connectivity, but their proprietary abstractions create lock-in through custom domain-specific languages, gated toolchains, and low ceilings.

Enterprises need deployment flexibility. Some want the vendor to handle hosting and environment management, either multi-tenant or single-tenant. Others need apps running on their own AWS VPC because they already have enterprise data access and security rules configured within that environment. Others still require full self-hosting, where they install, run, and manage everything themselves. Many enterprises need tighter security controls than are available in a multi-tenant SaaS environment.

AI-only app builders don’t offer this flexibility. Infrastructure is owned and operated by the vendor. AI code-generation tools are neutral here as well; they don't provide infrastructure. Low-code incumbents vary, but many push users toward broader vendor ecosystems with proprietary service dependencies.

The app you generate must run in production with enterprise-grade guardrails built in from the start. That means proper authentication, role-based access control, SSO, audit logging, version control, and staging environments. Platform engineers, professional developers acting as architects, codify these enterprise rules once. No matter which development mode a builder uses (natural language, visual canvas, or code), apps are secure by default because they run on a governed platform.

AI-only app builders are not designed for this. They’re prototyping sandboxes, not production platforms. They lack enterprise governance and audit trails. AI code generation tools require teams to build or buy the entire security and governance toolchain themselves. Low-code incumbents generally handle this well, having spent a decade building for enterprise IT.

Enterprise AppGen needs all three: trusted access to enterprise data, deployment flexibility to run wherever the customer needs, and security baked in by default, without the lock-in, governance gaps, or assembly requirements that limit each category individually.

Retool started with a specific bet: focus on professional developers who need to ship internal tools fast without sacrificing control. While other low-code vendors targeted citizen developers with visual-only builders, Retool focused on engineers who wouldn't compromise on code access, database connectivity, or deployment flexibility. That foundation now supports non-technical builders working alongside engineers on the same platform.

Retool’s IDE offers three first-class development modes: natural language, a visual canvas, and code. Builders can fluidly move between all three, choosing the mode that fits the task at hand. None of them is an escape hatch. They’re all full-fidelity ways to build.

This matters because natural language is not always the right abstraction layer. Just like LCAP forces users into a visual building approach, AI-only app builders force a single mode: natural language or nothing. Retool lets builders use whatever mode gets the job done, and flow between them without friction.

The platform handles governance, data access, and deployment. Builders focus on the problem at hand.

Say an operations lead is tracking inventory in a database, but the process for reordering stock is manual: check the numbers, email the warehouse, update the spreadsheet, and hope nothing falls through the cracks. A developer could build a proper tool, but it’s stuck in the backlog because it's “not a priority.”

With Retool, the ops lead opens a new app and types:

This single prompt defines the data model, UI behavior, and business logic in plain language. Retool generates the app, connects it to the live database, and inherits permissions from the platform guardrails configured by a developer, whose role has evolved to focus on architecting systems for business users to build safely.

The ops lead clicks a low-stock item, sees the detail panel, and clicks Reorder. The database updates. Done.

This took minutes, not days. It connects to real data, not a prototype. Because a developer had already configured the database connection and permissions, the ops lead couldn’t accidentally expose data or break anything, even if they tried. The guardrails are built in.

Generating an app from a prompt is the easy part. Running it in production, maintaining it over time, and scaling it across an organization requires the same discipline it always has, and often more.

AI has moved the bottleneck to the right. The initial creation of an app is no longer the constraint. The constraint is everything that comes after: upgrades, tweaks, keeping up with changing requirements, managing the infrastructure on which apps run, and maintaining apps over months and years. An enterprise AppGen platform that models the entire SDLC addresses this full lifecycle, not just the initial generation.

Get these right, and the math changes. More people build more apps without increasing the maintenance burden. Business teams stop waiting, developers stop context-switching, and the organization moves faster without losing control.

AppGen is the future, and it is coming at us extremely fast. The choice for teams now isn’t whether to embrace AI in building, but how to channel it to get apps, not code.

That is the future Retool is building toward. A platform where the same prompt that generates an app also inherits enterprise governance. Where professional developers and business engineers work in the same environment, with the same data, under the same security policies. Where you can start in natural language, refine in a visual canvas, and drop into code when precision matters, all without switching tools or losing fidelity.

Try Retool free or talk to our team to see enterprise AppGen in practice.