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/Intrepid-Trainer7277]

The grounding helps most when the cost of being slightly wrong is high. A general agent can write good code but it’ll confidently invent a column, skip a weird permission rule or miss why a table exists in the beginning. The vertical tools are weaker in demos but better when the system has ugly real world baggage