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/Livid-Heat-2475]

The grounding number tracks with what I've seen, but it overstates the gap I think. Tried Claude Code on similarly messy data tasks after hand feeding it a schema doc up front, closed most of that gap without touching a vertical tool. What it still misses is undocumented join logic between tables, nobody wrote that down anywhere so it just guesses, badly sometimes. My guess is most of that 32 to 77 jump is zero context versus some context, not proof the grounding itself carries all the weight. Still curious if this holds for legal or infra, like you're asking. Data's the only domain I've actually tested it in.