Those statements are mostly out of date and symptomatic of pre-agent-optimized LLMs. Opus 4.5 with clarifying rules in the CLAUDE.md does a good job at following idiomatic best practices in my experience.

That said, I'm mixed on agentic performance for data science work but it does a good job if you clearly give it the information it needs to solve the problem (e.g. for SQL, table schema and example data)

Not my experience. All frontier models I constantly test, agentic or not, produce code less maintainable than my (very good) peers and myself (on a decent day).

Plus they continue to introduce performance blunders.

Crying wolves, on day maybe there will be a wolf and I may be the last of us to check whether that's true.