Agentic ai: bu ne anlama geliyor
Continuous operation is presented as an agent advantage: no breaks, no handovers, consistent decisions at 3am. In a plant that runs shifts, this creates a specific friction that is rarely designed for.
The agent does not stop. The organisation around it does, three times a day, and each stop loses context that was never written down.
What handover actually transfers
A written handover records status: what ran, what stopped, what is scheduled. What it does not record is judgement — that a machine has been sounding wrong since Tuesday, that the material from a particular lot has been running rough, that a supervisor decided to hold something back.
The incoming shift picks this up in conversation, and much of it is never in a system at all.
Where the agent gets it wrong
An agent optimising a schedule at shift change sees the recorded state and none of the judgement. It reschedules the machine the outgoing shift was nursing. It releases the material that was being held back informally.
Each of these is correct given what the agent knows, and wrong given what the plant knows.
Three responses, in order of usefulness
Make the informal state recordable. A machine flagged as degraded, a lot flagged as suspect, with a reason and an expiry. This is a small data structure and it captures most of what goes missing.
Give the agent a handover window. Decisions with plant-floor consequences held for confirmation during the twenty minutes around shift change, rather than executed into a gap.
Record what the agent decided in the handover. The outgoing shift needs to know what the system did while they were focused elsewhere, in the same document they use for everything else.
The trust dynamic underneath
An agent that overrides a judgement the shift knew about and could not express does not just get one decision wrong. It teaches the shift that the system does not know what they know, and the response is to work around it — orders placed outside the system, machines quietly taken off the schedule.
Once that starts, the agent’s view of the plant is wrong in ways nobody reports, and the data it learns from is contaminated.
Why this is not solved by more sensors
Some informal knowledge is sensor-detectable and worth instrumenting. Much of it is not: it is inference from experience about what is likely to happen, held by someone who has watched the line for years.
The design question is not how to eliminate that knowledge but how to let it enter the system as a first-class input, with the person’s name on it, rather than as an override the agent treats as noise.
The measurable version
Track decisions the agent made within an hour of shift change and how often they were reversed. Concentration there is not a scheduling coincidence — it is the handover gap, showing up as a number.