Grounding AI agents in data you can actually trust
An agent is only as reliable as the governance underneath it. Most production incidents we've seen trace back to access and lineage gaps, not model quality.
When an AI agent gives a wrong answer in production, the postmortem usually starts by looking at the model. It should start by looking at the data the model was allowed to see. Most of the incidents we've traced weren't reasoning failures — they were governance failures wearing a model's name tag.
An agent grounded in a table with stale row-level access controls will confidently surface information a user shouldn't see. An agent grounded in three versions of the same customer table, none of which agree, will confidently pick whichever version answers fastest. The model isn't wrong to trust its inputs. The inputs weren't trustworthy.
This is why we scope applied-AI work as a data-governance project first and a model project second. Before anything gets wired to an agent, we want one governed, access-controlled, lineage-tracked version of the data it's allowed to touch — with an owner who can answer for it. The model work that follows is almost always the fast part.
The uncomfortable implication: if your data estate can't currently answer "who owns this table and who's allowed to see it," you're not ready to put an agent in front of it yet, no matter how good the model is.