The Real Bottleneck in Agentic AI

Manufacturers 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-in-four manufacturers plan to deploy agentic AI within two years, though only one-in-five 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 does not 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 match patterns the system has handled successfully many times, such as routine maintenance scheduling or reordering 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, organizations generally lack measures of how well a system needs to function in a given setting, 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, a senior IEEE member, described the current environment as a semiautomatic 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 reorder, every scheduling change, and every minor deviation, they stop reading requests closely and start clicking approve out of habit. The safety benefit oversight 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 miscalibrated.

Signals that governance Is actually working

A human in the loop setup can look functional while quietly providing false comfort. A short set of indicators separates real governance from theatre. The trend in escalation rate over time matters more than the raw number, since a rate that stays flat as a system handles more volume suggests the model is not learning the difference between routine and unusual cases. Time to resolution on escalated items indicates whether people are engaging seriously or rubber stamping to clear a queue. And the accuracy of the system's own uncertainty estimates, meaning whether the cases it flags as uncertain are actually the ones that turn out to need correction, is perhaps the clearest test of whether the escalation logic is calibrated at all.

Governance maturity across the industry still has room to grow. Broader enterprise research compiled this year found that only about one in five organizations have a mature governance model for autonomous AI agents, a gap that mirrors what is showing up on the manufacturing floor specifically. Closing it will depend less on better models and more on manufacturers building the organizational muscle to decide, deliberately and on a per decision basis, when a system should act and when it should ask.

(Source: Dijam Panigrahi, Co-founder and COO of GridRaster)