Advanced Automation: Intelligent Automation Team Workshop 1

Why is this important?

Having established a conceptual foundation in Intelligent Automation and Agentic AI, this session initiates the practical transition from theory to execution. The motivation is to transform abstract architectures into executable prototypes by leveraging team-defined scopes, assigned roles, and the capabilities of platforms such as UiPath, APIs, and generative AI. Students begin the iterative development of automation frameworks that reflect enterprise realities where scalability, governance, and adaptability must be considered alongside technical implementation. The class marks a key moment in moving from planning to coordinated action within a simulated enterprise setting.

What will we do?

This class marks the beginning of applied team development in the Intelligent Automation Challenge. Building on prior sessions where teams defined project scope and assigned roles, this workshop focuses on translating design concepts into initial automation components. Teams will work collaboratively using UiPath Cloud tools and generative AI services to prototype early-stage workflows. Instructor guidance will support architectural decisions, integration challenges, and alignment with the Technology for Good framework. Emphasis is placed on establishing a viable development trajectory that balances feasibility, creativity, and auditability.

Review and Extension:

We begin by revisiting key insights from the previous session on Intelligent Automation and Agentic AI, focusing on how modular architectures and agentic models enable dynamic task execution. Students will briefly reflect on their team 's assigned roles and initial process mappings, clarifying the distinction between deterministic RPA logic and agentic decision frameworks. This review reinforces the strategic importance of combining automation capabilities with governance and design thinking in a team-based setting.

This session initiates the first design sprint of the team challenge, and teams are expected to move from initial scoping toward early-stage automation buildout. In upcoming sessions, students will further refine these prototypes, incorporating agentic elements such as decision trees, feedback loops, and generative components. Progress will be reviewed through instructor consultations and informal peer checkpoints. Ultimately, the work completed during this and subsequent workshops will form the foundation of the Intelligent Automation Tournament, where teams will demonstrate operationally sound, intelligently guided bots with business relevance and strategic clarity.

Materials and Suggested Seating:

Case: Intelligent Automation Team Challenge Case.
Case: Technology for Good: Common Spring Project Case.
Case: User and System Prompt Example
Slides for this class have been archived for this quarter.
Analytics Tools: Business Process Modelling (BPM) software.
Automation Tools: UIPath: Cloud, Maestro, Studio Web
Automation Tools: Application Programming Interface (API) and Gen AI Tools including Chat GPT
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:

1. After a very brief review and some administration, the first half of class will include demonstrations of UIPath Cloud technology
2. The second half of class will be a team workshop, teams should aim to finalize their Generative AI components in this class.

To reinforce the generative AI materials covered in this three class module, I have curated a set of activities that can be used to explore the capabilities of generative AI. These activities are designed to be engaging and informative, providing students with hands-on experience in using generative AI tools. The activities can be found on the EYARC Experience website. To access the EYARC Experience you will need to sign up using an email, your UW NetID and the course code 11401-70454-29527. Instructions for logging on can also be found in this pdf.

The Experience site offers three modules, Introduction to Gen AI, Prompt Engineering (revision from our data analytics course), and a new Gen AI Governance module. I expect that everyone will cover the Governance Module in preparation for the two team projects (one person per team as a minimum). With a deadline of May 29th, any attempts made on the quizzes on the EYARC Experience platform will count towards professionalism.

In addition, I also recommend working through the Gandalf Gen AI Security Game by Lakera AI, that provides an interactive way of thinking about security related issues with prompt engineering. For our purposes, this game will help you think about how system prompts can help in establishing better responses from Gen AI, which is important when we are relying on it within an Agentic Automation framework. Submissions by May 22nd will count towards individual professionalism scores. How far you progress is not important, submit a screenshot to canvas of your progress.