# Where Automated Fixes Help — Safe Autonomous Code Fixing

Source: https://www.skillbyai.com/en/safe-autonomous-code-fixing/i-uses

> Repetitive, verifiable, well-scoped work.

## Good targets have a clear check

Coding agents and fix bots can now propose patches for failing CI builds, lint and type errors, simple bug reports with a reproducible test, dependency and security updates, flaky configuration and code migrations. They work best where success is **objectively checkable** (a test turns green, a scanner stops flagging, a build passes) and the change is **small and local**. They are poor at vague requirements, design decisions and fixes whose correctness no test can show.

## Speed without surprises

AI can propose fixes quickly, but only a disciplined pipeline makes those fixes trustworthy.

![Three ideas: use cases, risks, the safe pipeline.](assets/figures/safe-autonomous-code-fixing/section-1-map.svg) — Figure 1.1 — Use cases, risks and the pipeline.

## Suitability at a glance

Start with the left column.

```text
good candidates                                  poor candidates
failing unit test with a clear reproduction      "the app feels slow"
lint / type errors                               architecture changes
dependency bumps with advisories                 features without specs
deprecated API migrations (mechanical)           security design decisions
broken imports, typos, config mistakes           data migrations on production
```

## Start where checks are strong

Pick repositories and task types with good test coverage; automated fixes are only as safe as the checks behind them.

**Quiz:** Which task is the best fit for an autonomous fix bot?

- [ ] Making the app "feel faster" without a metric
- [ ] Redesigning the system architecture
- [x] A failing unit test with a clear reproduction
- [ ] Changing production data by hand

*Answer:* A failing unit test with a clear reproduction. Objective checks make automation safe.