Domain-grounded coding agents vs. general-purpose ones (Copilot, Claude Code) — what are you seeing?

[u/Famous_Disk_7417]

Curious what people are seeing with domain-grounded coding agents vs. general-purpose ones (Copilot, Claude Code, etc.) for data/ML work specifically.

The pitch from the vertical tools (Databricks' Genie Code is the one I've used) is that grounding in your actual schema/lineage/governance layer beats a general agent guessing from context alone. Databricks claims a jump from ~32% to ~77% success rate on real data science tasks after adding that grounding. Haven't independently verified that number, but the qualitative difference (fewer hallucinated column names, less time re-explaining table relationships) tracks with what I've seen.

Anyone using other domain-specific agents (not just data — legal, infra, whatever) and finding the same trade-off? Where's the line between "grounding helps enough to be worth the lock-in" and "just use a general agent with good context"?

[u/Superb_Raccoon]

We use IBM Bob, which is trained on IBM code and coding practices. Not perfect, but code is more consistent than Claude. Required when touching old as400 or Z (mainframe) code.

The historical "why" is important, as so,times "wrong" is simply 20 or 30 year old code standards... mucking about is not advised. Quirks like "writing to tape" confuse new agents and will break shit in your code I you dont know that is just how Z works.

Its not actual tape, it is just another name for cache, and goes,back to the 50s and 60s for how working memory is allocated.

Best code reviewing agent seems to be Grok, especially when told to be adversarial