Intelligent Agents

Regenerative AI algorithms will definitely not replace managers in making real-time operational decisions but may be very useful in providing advice based on data collected by smart operations management systems.

But real-time intelligent agent technology can be used to gather and interpret data from many sources into a centralized operations management database.

Where appropriate, other intelligent agents can then use this data to automate well defined decisions as well as to distribute this information to other systems and people.

Real-time intelligent agent technology was originally developed for applications such as advising pilots about how to complete their missions despite battle damage to their aircraft and today is used to detect counterfeit pharmaceuticals as well as contamination and bioterrorism in the food supply chain.

This intelligent agent technology is now being extensively deployed in industrial smart operations management applications.

Agents are independent computer processes, which can be run in parallel on a multi-core computer to handle many operational events at the same time.

These agents work together to solve problems iteratively and understand time and distance and use deterministic decision-making algorithms

These include:

• Rules
• Decision Trees
• Mathematical Algorithms
• Predictive Filters
• Clustering

which are understandable and independently testable, which make them ideal for use in an industrial environment, where operational control is essential.

With software platforms, such as WIPtracker and SmartOps24x7 agents can be easily configured for use in a wide variety of applications, using simple Python scripts, with the software platform providing over 90% of the needed code, pre-built or dynamically generated.