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For the better part of the last decade, the industrial and supply chain sectors have operated in the era of the collaborative robot, or cobot. These machines were heralded as the ultimate teammates, designed to handle the dull, dirty and repetitive tasks while safely sharing floor space with human operators. However, even the most advanced cobots have remained fundamentally tethered to human instruction — they excel at repetition, but falter at discretion, because humans traditionally make all the underlying decisions.
As we move through 2026, we’re witnessing a fundamental phase shift from standard automation toward agentic and physical artificial intelligence. We’re currently in a ramp-up period where robots are no longer just tools that follow a script; they’re beginning to dip their toes into the world of autonomous decision-making throughout the supply chain industry.
The Mechanics of Agentic AI
The transition from a machine that repeats to one that decides is powered by the convergence of large language models (LLMs) and vision language models (VLMs). In traditional setups, a robot’s path and actions were hard-coded or manually taught through physical guidance. Agentic AI changes the input source entirely, by digitizing physical systems to enable AI to operate within them.
Today, robots are being trained using the same materials used to train human workers, such as video inputs of expert human operators and digital instruction manuals. By processing video, robots can observe nuances in movement to learn functional patterns, while LLMs allow them to ingest textual manuals and technical documentation. By combining visual feeds with these instructions, the robot can use cues to guide its own actions, allowing it to interpret the necessary steps to complete a task from the manual itself.
While the ultimate goal is full autonomy across all physical tasks, the industry is currently finding its greatest success on the inspection side of manufacturing. This is particularly evident in high-stakes environments like welding, casting and forging. In a traditional workflow, a robot might perform a repetitive task, but a human must decide if the output meets quality standards. Agentic AI is now closing this loop independently by enabling robots to identify defects in complex structures and make the decision that a specific part is good or bad.
Once a defect is confirmed, the agentic system can autonomously create a task order in the repair queue, categorizing the specific issue and the necessary fix without human administrative input. This shift moves the robot from a simple laborer to an autonomous quality gatekeeper that no longer needs to come back to a human to take the next step in the process.
What AI Cannot Do Yet
Despite these leaps, it’s vital for executives to maintain a realistic view of the current boundaries of physical AI, as the technology is not yet at a stage where it can do everything a human can. The primary limitation remains human-like intuition in high-complexity physical tasks. For example, a human operator looking at a welding joint can instantly account for variables such as the size of a gap or specific temperature required based on the thickness of the material.
Currently, robots cannot independently decide how to perform these complex techniques, such as adjusting material feed or temperature on the fly, to account for non-standard variables. They can identify that a repair is needed, but they still rely on human-level decision-making for the actual execution of bespoke, highly technical physical repairs.
For leadership, the shift to agentic systems is an operational strategy to move toward autonomous physical systems. By allowing robots to take over decision-making in the inspection and task-generation phases, companies can reduce the bottlenecks often caused by manual oversight. As we look toward the remainder of 2026, the goal is clear: The digitization of the physical world is no longer about making machines work harder, but about enabling them to function with the same level of autonomy as the operators they assist.
Dijam Panigrahi is co-founder and chief operating officer of GridRaster Inc.
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