first off, drop the idea of coding "agents" entirely. semi-async death valley is not worth it, you will never get into the flow state with an "agent" that takes less than an hour to spin, and we did not learn how to make true async agents that run for this long while maintaining coherence yet. OpenAI is the closest in that regard, but they are still at a 20-minute mark, so I am not dropping the quotes for now.

another argument against letting LLM do the bulk of the job is that they output code that's already legacy, and you want to avoid tech debt. for example, Gemini still thinks that Kotlin 2.2 is not out, hence misses out on context parameters and latest Swift interoperability goodies. you, a human being, are the only one who will ever have the privilege of learning "at test time", without separate training process.

replace coding "agents" with search tools. they are still non-deterministic, but hey, both Perplexity and Google AI Mode are good at quick lookup of SvelteKit idioms and whatnot. plus, good old Lighthouse can point out a11y issues - most of them stem from non-semantic HTML. but if you really want to do it without leaving a terminal, I can recommend Gemini CLI with some search-specific prompting. it's the only CLI "agent" that has access to the web search to my knowledge. it's slower than Perplexity or even ChatGPT Search, but you can attach anything as a context.

this is the true skill of "how to use AI" - only use it where it's worth it. and let's be real, if Google Search was not filled with SEO crap, we would not need LLMs.