How AI Coding Works

AI coding works by having a large language model generate code one token at a time, then wrapping that generation in a disciplined pipeline: plan, write, compile, verify. The model proposes; the tooling verifies. That verification loop is what separates a demo from software you can actually use.

In plain English

AI doesn't "know" code the way a developer does — it predicts the next character, like autocomplete on steroids, and the platform around it does the real verification. The model drafts; the typechecker, the sandbox, and the production gate approve.

A model that confidently produces code is the tech version of a friend who gives directions with total certainty. Lovely to have in the car. Still double-check the map.

What the model does

At its core, an AI model is a probability engine. Given your prompt and the code written so far, it predicts the next token — a fragment of a word or symbol — and keeps going until the response is complete. That's how chat completions work, and it's also how code gets generated.

But raw generation isn't enough. A model can confidently produce code that doesn't compile, references missing imports, or looks right and behaves wrong. Production AI coding systems add a layer of machinery around the model to catch those failures.

What the tooling adds

Real AI coding tools don't just print code into a chat window. They act on a real system:

• Planning — the agent proposes a file manifest and phases before writing anything.
• File writes — it creates actual source files in a project directory.
• Typechecking — it runs the compiler and reads the errors.
• Fixing — it applies surgical edits and re-checks.
• Running — it starts a dev server and probes it over HTTP.
• Verifying — it confirms files exist on disk rather than trusting its own claims.

This is why reliable tools execute inside a cloud sandbox. Orbit runs builds in a 1-hour cloud sandbox where the agent can install dependencies, run commands, and serve a preview without touching your machine.

Tool calls: how agents act

An agent "acts" through tool calls — discrete operations like writing a file, installing a package, or running a command. The model decides which tool to call, the system executes it, and the result comes back as new context for the next decision.

That loop repeats. In Orbit, a chat turn allows up to 25 tool calls, and a build allows up to 220. Each turn also has a 10-minute timeout, so a model that loops on a broken approach gets stopped instead of burning your time and credits.

Why typechecking changes everything

TypeScript is one of the most valuable verification layers in AI coding. When the agent writes a component and then references it in a file it never created, the typechecker catches the dangling import immediately. The agent can then fix it — or wire up orphan components in a dedicated pass.

Orbit runs an inline typecheck after generation and lets the agent apply one surgical fix turn before the deeper heal loop. That single step removes a huge class of "looks done but broken" builds.

The production gate

Before a build ships, serious tools run a production gate: a full typecheck plus a production build. Orbit's gate also scans for leaked secrets — a service-role key or a Stripe secret in a client path blocks the ship. If the typecheck passes but the production build fails, the preview can still ship with an honest "build passed with warnings" label rather than a false success.

What this means for you

AI coding is best understood as a collaboration: the model drafts at machine speed, and the system verifies at compiler speed. Your job is to steer and review. Expect the agent to do heavy lifting, and expect the platform to catch the obvious mistakes — so you can spend your review on design and logic instead of syntax.

Try it and compare

Ask your current AI to write a small TypeScript app and check whether it actually compiles. Then ask Orbit the same thing and watch it run a typecheck, apply a fix pass, and gate the preview before you even see it. Compare how many "trust me, it works" moments each one produces.

FAQ

Q: How does AI actually write code? A: An AI model predicts code one token at a time based on its training and the conversation context, but production tools wrap that in a pipeline of planning, file writes, typechecking, and sandbox verification.

Q: Why does AI coding need a sandbox? A: Because output must be verified, not trusted. A sandbox lets the agent run the code, typecheck it, start a dev server, and confirm files exist before showing you a preview.

Q: Can AI code be trusted without review? A: No. AI-generated code can compile and still be wrong, so reliable tools gate shipping behind typechecks, production builds, and security scans before they call a build done.

Q: Why do AI coding agents use tool calls? A: Writing files, running commands, and checking results are discrete actions. Tool calls let the model act on the system instead of just describing what it would do.

Build it on Orbit

Orbit pairs LLM generation with typechecking, sandbox execution, and a production security gate — so the code you get has already survived the verification loop. Start building on Orbit.