Summary of "AI Controls In Manufacturing"

Summary

This document contrasts administrative controls (human-readable policy) with engineering controls (automated enforcement), and shows a practical demo of converting a written downtime-entry policy into agentic automation that produces consistent, standards-compliant MES entries. It summarizes definitions, tools used, demo steps, best practices, business value, and speakers/sources.

Core idea

Administrative controls are the starting point: define “what good looks like” as policies, SOPs, or data-entry standards.
Engineering controls implement those policies in code, agents, PLC logic, or other automation so outputs are consistent and enforceable.
Building automation before defining clear standards produces inconsistent and low-trust AI outputs. Manufacturers should first document precise, testable administrative controls, then convert them into engineering controls.

Definitions and concepts

Administrative control: A documented policy or rule (SOP, MES data-entry standard, system prompt). Typically human-dependent and intermittently enforceable; observed human data fidelity is roughly ~60%.
Engineering control: Technical implementations that enforce policy (machine guards, PLC logic, software/agent automation). These reduce reliance on human memory/compliance and produce consistent, standards-compliant outputs.
System prompt: When used as rules for an agent, a system prompt functions as an administrative control.
Agentic AI: Autonomous agents that read inputs, apply policy, and produce outputs without manual enforcement.

Practical tutorial / demo

Use case: downtime-reason entry for MES. A 7-page administrative-control document specified required fields and formats:

Required fields: downtime category, primary cause, action taken, notified parties, contributing factor, operator ID, duration.
Format rules and examples were included.

Platform and tools used:

Ajanet AI (agentic AI)
UNS Studio / UNS Browser (agent and tag browser)
MES tags and simulators
Tag-change triggers, get-tag tool, and script engine

Demo highlights:

Comment formatter (simple demo)
- Create an agent with a system prompt:
  
  “Take raw comment, return only a professional formatted comment.”
- Test: raw operator text -> agent returns a professionalized comment.
Downtime formatter (engineering-control demo)
- Create an agent triggered on tag change (line status).
- Agent reads specific MES tags (category, cause, action, operator ID, duration, etc.).
- Agent writes a standardized, policy-compliant formatted entry into a formatted_comment tag.
- Result: when the simulator sets line-down and populates tag values, the agent produces a complete downtime narrative (category, primary cause, action, notifications, contributing factor, status).

Implementation notes:

Agents can both read tags and format output; alternatively, scripts can call agents (demo used the agent to read tags for clarity).
Tag-change triggers and get-tag operations pull MES values and write payloads to the formatted comment tag.

Best practices and warnings

Define AI strategy and administrative controls first. Precise, testable standards must exist before engineering an agent to enforce them.
Avoid vague engineering prompts (e.g., “summarize complaints”) without acceptance criteria — this yields inconsistent, plausible-but-unreliable outputs and erodes trust.
Use agents to augment humans (make tasks easier) or automate repetitive, low-fidelity tasks (verify vs fully replace operator input).
Administrative controls are a prime source for selecting automation use cases; they reveal desired outcomes and the reasons behind processes.

Business value

Converts low-fidelity, human-entered data into high-quality, consistent records.
Improves downstream analytics (OEE, RCA, maintenance planning) by reducing “garbage in.”
Allows operators to verify machine-crafted entries rather than manually producing them, which reduces human error and variability.

Tools and features demonstrated

Ajanet AI / agentic AI for autonomous formatting and enforcement
UNS Studio: agent definition, prompts, trigger types (on-demand, tag-change), and test runner
UNS Browser / simulator: MES tag simulation and formatted-comment tag writes
Tag-change triggers and get-tag operations for pulling MES values and writing payloads

Main speakers and sources

Video presenter / host (author of the demo and commentary; references prior video “AI isn’t helping”)
Ajanet AI (agentic AI product/concept used in the demo)
UNS Studio / UNS Browser (platform used to build and run agents)
MES system, PLCs, and shop-floor data (context and data sources used in the examples)