We detect intent drift AI tools miss.
AI-assisted work can look correct while moving in the wrong direction.
Most AI evaluation asks whether the output is correct.
But a correct output can still serve the wrong objective.
Work survives.
Reviews pass.
Decisions move forward.
Yet the original intent has already drifted.
The visible burden is review.
The hidden mechanism is intent drift.
Most teams ask:
Is the answer correct?
A different question matters:
Is the work still serving the objective it was supposed to serve?
As AI-generated work increases, that question becomes harder to answer.
Wrong work can survive for a long time when it still appears reasonable.
The cost often appears later:
More reviews.
More re-checking.
More uncertainty.
More operational friction.
Review debt is the symptom.
Intent drift is the mechanism.
HeadTabs is building intent-drift detection for AI-assisted work.
Our first proof domain is AI-assisted engineering.
We focus on environments where work can be independently judged by experts and supported by physics-based evidence.
Because assurance requires more than output.
It requires evidence that the work still serves the intent it was meant to serve.
If AI-assisted work is already creating review debt in your team, leave your email.
Get field notes on intent drift and AI Work Assurance.