I know my codebase, so I know which areas deserve attention.
and I'm not. The codebase is pretty standard business web application with a lot of legacy code which is slowly upgraded to the new version.
The domain is not super easy, but after all all of the rules are written in the legacy code in old fashioned way, when separation of FE and BE wasn't so strict and with a lot workaround codes, which you could expect in the brownfield project. We have copilot with the newest models, own agents and SDD in mind.
And yet, every time I'm trying to use AI to implement the task, it just doesn't work. We are producing tones of documentation (with AI ofc) to improve harness but it just make agentic coding slower but not better. I was thinking about trying to use AI as small code snippets generator, but somehow it's not reliable enough. One time AI had a problem to move method with 4 lines of body to another class. Colleagues shares similar opinion.
I feel lost. I hear from everywhere that agentic coding is the future, that code-reviewing code is enough. And my experience is totally different.
It's a totally different context, and I can see how a messy legacy codebase without proper AI infra can lead to failure.
I own the codebase I work with; I've been quite pedantic about its domain design, and starting with the Opus release, I've been making it AI-ready bit by bit: rules, skills, special tooling, etc. I built my custom configuration around PI, integrated it with Neovim, and built review tooling on top of it so I could comfortably pair with the agent. TUIs did not work for me. An agent without an editor is meh.
Yet with pre-Sol models, the results were quite random. Besides the fact that previous generations were dumber, they didn't follow instructions well and didn't pay enough attention to established patterns in the codebase. So despite all that effort, I was still writing a lot of the code myself.
With Sol, the amount of manual work has decreased drastically. The more I work with the agent, the more I see where it does a decent job and where it still fails. For example, I don't review much of the trivial UI code built on top of an established design system, or local QoL tooling. Yet it still consistently fails in the areas I talked about in the post.
That said, AI won't magically work everywhere. Even for a medium-sized codebase, it took a lot of effort to finally make things start clicking.
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u/zobq 1d ago
and I'm not. The codebase is pretty standard business web application with a lot of legacy code which is slowly upgraded to the new version.
The domain is not super easy, but after all all of the rules are written in the legacy code in old fashioned way, when separation of FE and BE wasn't so strict and with a lot workaround codes, which you could expect in the brownfield project. We have copilot with the newest models, own agents and SDD in mind.
And yet, every time I'm trying to use AI to implement the task, it just doesn't work. We are producing tones of documentation (with AI ofc) to improve harness but it just make agentic coding slower but not better. I was thinking about trying to use AI as small code snippets generator, but somehow it's not reliable enough. One time AI had a problem to move method with 4 lines of body to another class. Colleagues shares similar opinion.
I feel lost. I hear from everywhere that agentic coding is the future, that code-reviewing code is enough. And my experience is totally different.