What Our AI Agent Still Can't Do

Most AI tools only tell you what they're good at. That's how you end up discovering the gaps in production. So here's the other half: where our AI agent still misses, specifically, and what we're doing about each one.

What it's genuinely good at

• Targeted bug-fixing (~85–90%) — given real broken code, it produces a fix that compiles clean and makes the specific change.
• Simple full-stack builds — it scaffolds working SPAs reliably.
• Prompt → full-stack app → live deploy with a real database — validated end to end.

Where it still misses

From our own correctness-gated evals, the honest gaps:

• Complex full-stack scaffolding. On a notes app with auth, it built the database schema but missed declaring the bcrypt / jsonwebtoken dependencies — the kind of gap that compiles in isolation but breaks the build.
• Run-to-run consistency. A landing-page fixture's dev-server check passed on one run and failed on another. Non-determinism is real, and we measure it instead of hiding it.
• Small correctness slips. Using React.Fragment without importing it; a to-do app that skipped localStorage persistence. Individually minor — exactly the sort of thing you'd want to catch in a diff review, not in production.
• Read-only tasks. On a task that only asked it to find and report something, it wrote files instead of answering. Knowing when not to act is its own skill.

What we do about it

Every genuine failure feeds a closed self-improvement loop: the run is analyzed, a prompt change is proposed, and the candidate is validated against the same correctness gate — with a holdout set guarding against regressions — before anything reaches production. Some of the misses above already have fixes in flight; some don't yet. We publish both states.

Why publish this at all

Because you're going to hit these limits either way — the only question is whether you find out from us now or from a broken build later. It's also why NoCoder puts diff review at the center: the agent isn't perfect, so you stay in the loop. Every change is a per-file diff you approve before it applies.

The short version: Our agent is strong at bug-fixing (~85–90%) and simple full-stack builds, and weaker on complex scaffolding, consistency, and a few specific tasks (missed auth deps, a skipped localStorage step, a Fragment import, a read-only find task). We fix what we can via a validated self-improvement loop and publish the rest. See how we benchmark or try NoCoder free.

---

This post originally appeared on the NoCoder blog. NoCoder is an AI full-stack app builder that shows every change as a diff you approve before it ships — build at AI speed, ship only what you've reviewed. Free to start on free models.