Agentic AI in Manufacturing:
from private AI to trusted action
Your AI agent passed the plant demo. Now it wants access to your ERP.
A practical framework for agentic AI in manufacturing: keep design and plant data under your control, decide which records an agent may change, and prove the whole workflow before it touches production systems.
Drag to rotate Key takeaways
Four rules for your next industrial AI project
- 01 Set data boundaries before choosing a platform Define where plant and design data may be processed, who can access it, how models can be changed, and what the provider must hand over if you leave.
- 02 Put policy checks between agents and ERP/MES Prompts cannot enforce access. Check permissions, workflow status, and approval before every action, then validate the result.
- 03 Test the failures your teams actually face Use real cases such as missing evidence, conflicting records, a timeout after a write, or permission removed mid-task. Run them before an agent leaves the pilot.
- 04 Measure cost per accepted outcome Count a work order, deviation, or claim only after the business accepts it. Include review, rework, and recovery in the cost.
From private AI
to trusted action on the factory floor
Free to read. See why a prompt can't stop an agent, and what actually can.