Focus: AI governance


The real bottleneck in agentic AI is not the model, it is the handoff


By Dijam Panigrahi, Co-founder and COO of GridRaster M


anufacturers have spent the past two years proving that agentic AI can work on the factory floor. Systems now flag a failing bearing, schedule the


repair, reorder the part and update the production plan without a person touching a keyboard. According to Deloitte’s 2026 “State of AI in the Enterprise”


report, nearly three quarters of manufacturers plan to deploy agentic AI within two years, although only 25% currently have a model equipped to do it reliably. That gap says something important. The industry has largely solved the technical half of agentic AI. What remains unsolved is the harder, more human half: deciding exactly when a system should keep running on its own and when it should stop and ask someone. Get that decision wrong in either direction and it shows up


on the balance sheet. Too much oversight and operators start ignoring alerts entirely. Too little and a confident system acts on a judgment call the moment conditions shift, and the error compounds before anyone notices. Neither failure mode looks dramatic in the moment. Both are expensive over time.


Why binary oversight doesn’t work Many manufacturers still treat human oversight as a single switch, either the AI runs autonomously or every action requires sign off. That framing does not match how decisions actually vary on a production line. Reordering a routine consumable and halting a line because a sensor reading looks unusual are not the same category of decision, yet they often get funneled through the same approval process. A more workable approach classifies decisions into three


tiers based on risk and novelty, rather than treating oversight as all or nothing: “Proceed” covers decisions that are low risk, and matches patterns the system has handled successfully many times, such as routine maintenance scheduling or re- ordering parts within established thresholds. “Pause” covers decisions that carry moderate risk or fall slightly outside familiar patterns, where the system continues gathering information and flags its reasoning but waits briefly for confirmation before acting. “Escalate” covers decisions that are high risk, unusual, or both, where a person reviews before anything happens on the floor. As Stanford’s 2026 AI Index Report noted through HAI


researcher Ian Perrault, organisations generally lack measures of how well a system needs to function in a given setting,


08 November 2026 www.electronicsworld.co.uk


What remains unsolved is the harder, more human half: deciding exactly when a system should keep running on its own and when it should stop and ask someone


which is precisely why risk and novelty, not blanket policy, should determine the tier. This is also consistent with what practitioners are seeing


in the field. Ramakrishna Garine, AI specialist, described the current environment as a semi-automatic human in the loop state, where human validation is still needed for unplanned scenarios, even though full-function agentic systems already exist. The three-tier model gives that instinct a structure instead of leaving it to case-by-case judgment.


Why over monitoring backfires just as badly It is tempting to assume more human checkpoints always mean more safety. In practice, oversight has a saturation point. When operators are asked to approve every re-order, scheduling change and minor deviation, they stop reading requests closely and start approving out of habit. The safety benefit oversight it was meant to provide quietly disappears, replaced by the appearance of control. Industry data on human-in-the-loop deployment supports


this. Analysis from DigitalApplied’s 2026 enterprise agent research found that oversight rate should be treated as a production trust metric in its own right, since a workflow with a low escalation rate paired with strong adoption behaves very differently than one with a high escalation rate and weak adoption, even when both are technically live in production. On a factory floor, an escalation queue that grows without a matching rise in decision quality is not evidence of caution. It is evidence that the thresholds are mis-calibrated.


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