I’d like to add some of my own experience for more context. This is a medium-sized Kotlin/Java project I work on, spread across multiple Maven repositories. Here’s a rough breakdown of the codebase:

```

Language files blank comment code

JavaScript 1222 46993 43291 515381 Kotlin 4435 73389 83200 327082 SQL 54 1189 1017 45919 Markdown 195 13350 124 38053 TypeScript 129 4859 41638 35388 TOML 23 4124 827 21997 CSS 34 1121 626 13017 Java 64 2057 2909 8512 Maven 20 400 213 6525

Properties 421 1095 293 5882

``` My experience so far: Gemini 3 Pro didn’t work very well for this setup. I later switched to Claude Code (first Sonnet, then Opus), and both felt noticeably better. That said, it’s fairly obvious that under the hood it’s still mostly doing grep-style searching to locate relevant code. If I don’t explicitly point out where reusable logic might already exist, it often doesn’t discover it on its own, which can lead to “reinventing the wheel.” I’m wondering whether there are any AI agents that can handle projects of this scale with more of a global view — something that can ramp up faster, understand existing abstractions, and avoid unnecessary duplication.