Advanced Automation: Intelligent Automation and Agentic AI Concepts

Why is this important?

While traditional RPA offers significant efficiency gains by automating structured, rules-based tasks, it is increasingly important for students to understand how automation frameworks are evolving. Intelligent Automation (IA) and Agentic AI represent the next frontier, integrating machine learning, document intelligence, and generative AI into workflows. This class introduces students to the expanding capabilities of automation systems, motivating a transition from purely procedural bots to adaptive, decision-capable agents capable of completing complex sequences of tasks.

What will we do?

This class marks the first formal introduction to Intelligent Automation and Agentic AI. Students will explore how traditional RPA can be extended through the incorporation of AI components that enhance perception, classification, and decision-making within automated workflows. Further, students will be exposed to agentic models wherein a bot operates semi-autonomously through a series of interrelated rule-based and intelligent sub-tasks. The session will also serve as a launching point for a future group project where students will design and implement agentic workflows. Initial hands-on activities will emphasize understanding how these technologies expand the scope and impact of automation beyond static rules.

Review and Extension:

Building upon prior work in developing structured RPA bots, this class will briefly revisit the architectural foundations of automation, emphasizing the distinction between deterministic and probabilistic decision pathways. Students will connect earlier experiences with variables, DataTables, and loops to the more dynamic architectures enabled by Intelligent Automation. A comparative review will be conducted to highlight both the continuity and the discontinuity between traditional RPA and emerging IA systems.

This class lays the conceptual groundwork for a future group project focused on designing agentic automation systems. In subsequent sessions, students will be tasked with identifying business processes that benefit from the integration of machine learning models, generative AI, or rule-based decision chaining. Practical development efforts will focus on constructing bots that combine traditional RPA capabilities with intelligent enhancements, preparing students to prototype and evaluate hybrid automation solutions that mirror the future direction of enterprise automation ecosystems.

Materials and Suggested Seating:

Case: Technology for Good: Common Spring Project Case
Slides: will be available for download by the beginning of class in either powerpoint or pdf formats.
Suggested in-class seating: during this class, please sit in your assessment teams.

Note on Pre-Class Preparation:

There is no suggested preparation for this class.

Class Plan: