NEUPAC™ on the Factory Floor: Governing Agentic AI Across Manufacturing Operations

Plants are being populated with specialized AI agents much faster than governance can keep up with. This is not because we need fewer agents, but because we need one perimeter for all of them.

Ask a manufacturing CIO how many agents are active in their factories, and chances are they will give you an estimate rather than an exact number.  One line has a vibration anomaly model deployed. Another line is using a defect classification agent. Demand forecasting is tested in planning. Supplier risk assistance has been developed within procurement without approval from the IT department.

Nothing wrong with it; however, every single one of those agents was used by people who were aware of the problem and needed the quickest solution. Nevertheless, when all is summed up, it becomes evident that this is the environment of autonomous software involved in production planning, asset management, and supplier selection, lacking a common identity model, audit traceability, and cost control.

According to Gartner, the average Fortune 500 company globally is expected to have more than 150,000 agents deployed by 2028, up from only 15 in 2025. Manufacturing has its share in the steep part of this graph.

Why Containment is the Wrong Reflex

Containment means letting only a small number of tools through, rejecting the rest, and passing everything via centralized IT, on a shop floor that runs faster than anything else.

The strength of an agent in a manufacturing context is often found in its specificity. The yield optimization agent was created for the chemistry of the process line. The quality agent was designed based on the defect categories in a specific product line. The predictive maintenance tool is optimized for the failure patterns in specific assets. Combining them all into a single generic tool loses their specificity.

This could be solved by abandoning any standardization of agents in favor of standardization of the control mechanisms around them. That is the key idea behind Bring Your Own Agent (BYOA) technology in NEUPAC™, the patented enterprise AI platform developed by YASH Technologies. The agent retains its individuality and specificity. The platform provides the boundary that every agent passes through: who built it, what data it accesses, when it needs human approval to perform actions, and how much it costs to operate.

What Governed Means on the Factory Floor

Operational governance is not a policy paper. It is machine-speed controls.

• Zero Data Copy architecture: Which means agents operate across MES, historian, and ERP without duplicating process data in a system other than the system of record.
• Role-based access control: Applies to both agents and users, meaning that a maintenance agent has read access to asset telemetry and will not make it to the purchase order queue.
• Governance and observability in real-time: Recording every action taken by agents, their decisions, and policy enforcement events in real time, meaning that the audit trail is there rather than constructed after a quality issue.
• FinOps for AI: Tracking token and inference costs by plant, function, and initiative with enforceable budget guardrails.
• Responsible AI scoring: Based on a configurable Trust Score for every deployed agent assessed against eight criteria depending on its use case.
• Pre-built connectors to SAP, Oracle, Salesforce, and ServiceNow, which means that a newly created agent gets governance by virtue of being plugged in.

Good Governance is Not a Choice Against Good Performance

An effectively governed agent — working within clearly demarcated roles, being watched for trustworthiness, accountable for the expense — is the more reliable one. The following are results reported by NEUPAC™ in manufacturing implementations.

These are measures that plant management already tracks; hence, they are the correct measures for an agent portfolio to benchmark against.

From One Plant to the Whole Network

Most manufacturers do not have an agent problem at a single site. There are replication problems within a group. A predictive maintenance agent that works well in one location should work well at another eleven sites without being recreated, redeployed, or reviewed each time.

This is exactly the type of system NEUPAC™ is designed for – multi-tenant management for group companies, reusable agents, agentic processes where the workflow connects multiple specialists into one process, and a single layer of access from web, Microsoft Teams, Slack, and WhatsApp, where the shift supervisor and the plant manager operate in the same governed environment.

The companies that succeed will not necessarily have the largest number of agents, nor will they hesitate to deploy because of potential risks. Those who succeed will be able to deploy new agents without hindering progress and still know at any moment what these agents do, who authorizes their actions, and what costs they entail.

To learn more about NEUPAC™ governance, optimization, and scaling of agentic AI in manufacturing, please book a demo on neupac.ai.

• Agentic AI
• Artificial Intelligence
• Bring Your Own Agent