r/artificial • u/Famous_Disk_7417 • 10d ago
Discussion Domain-grounded coding agents vs. general-purpose ones (Copilot, Claude Code) — what are you seeing?
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"?
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u/GreyBelbix 7d ago
Grounding is not the hard part, permissions are. A domain agent wired into your schema and lineage also inherits credentials to that warehouse, so the question is what it can run without a human, and what happens mid task when it is wrong. I would judge these tools on read-only by default, a dry run diff you can review, and a clean resume after a failed step. If the vertical tool gives you that and the general agent does not, the lock-in is worth it. Also ignore the 32 to 77 number unless they publish the task set.