We’ve watched more than one team build a brilliant orchestrator on top of a terrible database.
The agent worked. That was the problem. It read the stale field, trusted the duplicate record, took the side of whichever system it queried first when two of them disagreed about the same fact — and then executed on all of it, confidently, at scale. The model didn’t fail. It faithfully amplified everything wrong underneath it.
Before AI, those data problems were survivable because humans quietly absorbed them. People knew which report to distrust and which field hadn’t been updated since the migration. That reconciliation never got written down; it lived in heads. An agent doesn’t have a head to live in. It reads what’s there and believes it.
Which is why the first question in any agent engagement isn’t “what should the agent do?” It’s “what does the agent read — and is it true?” Establish the source of truth, resolve who wins when systems disagree, and decide what the agent does when the answer is missing rather than wrong. That work is unglamorous. It’s also the entire difference between automation and automated error.
Ground truth before intelligence. The order isn’t negotiable — intelligence pointed at a fiction just gets you to the wrong place faster.