Much of the discussion around agentic AI jumps quickly to the end state: full autonomy.
But most enterprises are not starting from scratch.
They are evolving from deterministic systems that did more than coordinate work. They encoded accountability: how work gets assigned, how decisions get traced, who answers when something goes wrong, and how recovery happens.
Dorsey and Botha argue that hierarchy was the best available solution to a coordination problem that AI may now solve differently. That feels directionally right.
But in enterprises, hierarchy and deterministic workflows did more than coordinate. They carried accountability and control. They also embedded decades of enterprise logic — technical, procedural, and organizational — that autonomy alone does not untangle.
So this is not just a coordination shift.
It is a control shift.
We are moving from systems where every path is encoded explicitly to systems where intent is defined, boundaries are set, and the system determines the path. Not less control. A different kind — and it demands a different discipline.
What has brought this into focus for me is how quickly decision traceability starts to blur once the system is no longer following an explicitly encoded path. The output may still look reasonable, but the basis for the decision becomes harder to inspect, challenge, and reverse. And because the model itself can advance, drift, or regress, that surrounding traceability matters even more.
Enterprise AI is less about the model and more about the surrounding system. Accountability does not transfer automatically. It has to be rebuilt deliberately — through traceability, reversibility, bounded action rights, and decision frameworks that expand as reliability is demonstrated.
The end state may still be greater autonomy. But the path likely starts with the surrounding architecture: a governance and control plane, a context layer that evolves with organizational reality, and bounded domains where autonomy can be proven before it is broadly expanded.
The near-state is not defined by how much autonomy can be achieved.
It is defined by how much transformation can be absorbed responsibly — without breaking trust, accountability, or recoverability.
First published on LinkedIn, April 22, 2026.