AI is writing more of your code every month. That's not the hard part anymore. The hard part is understanding and trusting what it built well enough to ship it. When an agent generates hundreds of lines of backend logic, someone still has to review it, and reading raw code line by line doesn't scale to the speed AI now moves at.
This is where a visual layer changes the equation. When everything AI builds lands somewhere your whole team can actually read and edit, you can iterate quickly and still be sure of what you're putting into production. Migrating to Xano isn't just modernizing your stack, it's positioning yourself to build and iterate with AI safely.
To show what that looks like end to end, we migrated a real, enterprise-class Java codebase to a Xano backend, then pointed AI at the freshly migrated API endpoints to prove the business logic actually works. Read on to learn more, or watch the video below.
[YOUTUBE:mJehZ25UFFQ]
Why migrate incrementally (and why this demo didn't)
In the real world, you almost always want to migrate one service, or one piece of a service, at a time. It keeps blast radius small and lets you validate as you go. For this demo, though, we migrated a whole repo at once to show the full picture: the Broadleaf Commerce framework, an openly available Java project with genuinely complex e-commerce and checkout business logic.
That complexity is the point. E-commerce checkout, cart repricing, promotion stacking, and coupon validation are exactly the kind of gnarly, rules-heavy code that makes legacy modernization risky. If the approach holds up here, it holds up on most enterprise workloads.
The migration process, phase by phase
The workflow uses an AI coding agent in the IDE of your choice, paired with the Xano Developer MCP, which connects the agent to official Xano documentation and the XanoScript language server so it generates backend logic grounded in how Xano actually works rather than generic guesses.
The prompt sets some ground rules and then walks the agent through distinct phases:
- Phase 0, domain map. Clone the repo locally and have the agent build a map of the existing codebase.
- Phase 1, feature specs. Run a full repo analysis to extract feature specs straight from the existing code, contracts, and docs, exactly as they exist today.
- Phase 2, Xano specs. Map those feature specs to Xano specs using the Developer MCP.
- Phase 3, build. Have the agent build the actual Xano backend against those specs, writing in XanoScript, Xano's backend scripting language.
In the demo, Claude Code in VS Code worked through phases 0 to 2 on its own: it understood the Java Broadleaf repo, produced the domain map, generated feature specs against the Java code, and then translated those into Xano specs. Because XanoScript keeps code and visual logic in sync, whatever the agent writes stays readable and reviewable no matter who, or what, wrote it.
From local files to a live backend
Phase 3 is where the backend takes shape. Building against the Xano specs, the agent produced functions, tables, background tasks, APIs, and API groups locally, all derived from the original business rules in the Java repo.
Getting that live is a CLI operation. After authenticating and selecting the target workspace and instance, a single push sends the local files up to Xano. Everything that existed as local files, tables, functions, and endpoints now maps one to one to what's running in the workspace, on production-grade Postgres with auth, background jobs, and deployment already handled. There's no separate DevOps step to wire up.
Watching AI exercise the migrated logic
The real test isn't whether it compiles, it's whether the business rules behave. So we opened a fresh chat and had AI hit the live Xano backend through two workflows: adding products to a cart, and processing promotion codes with a simulated payment.
The agent signed up a brand-new user, logged in, and started calling endpoints. On the cart side it added a SKU, calculated the cart total, changed a quantity from two to three, and removed the item so the cart was empty again, each step hitting the same cart-items endpoint backed by a real function in Xano. Notably, it reported roughly 47% less code to produce the same behavior compared to the sprawl of Java services it replaced. That's the boilerplate getting abstracted away, leaving less to review and more focus on the actual business rules.
The promotions scenario stacked multiple coupon codes, removed codes, surfaced the right error when a code was re-entered ("this offer code's already applied to your cart"), and placed an order with a simulated payment. Same result: the rules checked out, with a similar reduction in code versus the Java original.
Governing it all in the visual layer
Here's the payoff. Every one of those endpoints is visible in Xano's canvas as a readable flow, not a wall of code. The add-to-cart logic shows itself validating the SKU, checking inventory, creating the cart if it doesn't exist yet, and repricing depending on whether it's the first item or the fifth. The offer-code logic shows the input, the database lookup, the existence and expiry checks, the guard against applying the same code twice, and the final reprice.
Because it's all in one place, you can watch live requests come through, inspect the endpoints that were hit, and see errors as they happen. When something fails, the error logs let you jump straight into the stack and land on the exact step, in the demo, a precondition that failed because the call wasn't a valid request. Instead of stitching together separate tools to monitor a black box, you govern and understand the backend visually, in the same environment where it runs.
And it stays editable. The canvas view, the function stack view, and the code view all map to each other one to one. You can edit a precondition in the visual layer or drop into XanoScript, and the other views update to match. AI generates the logic; the visual documentation layer on top means you can see, validate, and understand exactly what's running.
The takeaway
In one flow, we used AI to analyze a Java repo, extract its specs, map those to Xano specs through the Developer MCP, and rebuild the business logic on a live, production-grade Xano backend, with far less code and a visual layer the whole team can read.
If you want to try the workflow yourself, the Developer MCP and CLI are the place to start.
