goAAIA (Agentic AI Automation)

About course

Course description

goAAIA (Agentic AI Automation) is a four-day programme to equip participants with a practical understanding of how agentic automation systems are designed and applied in real-world contexts.

The course is delivered in two distinct phases:
• Two-day classroom facilitated learning
• Two-day immersive project consultancy and implementation session

Participants will develop a working understanding of how AI capabilities are integrated and controlled within low-code automation platforms, enabling automations to move beyond static rules toward systems that can reason, decide, and act under guidance.

The curriculum introduces the key building blocks involved in creating agentic automation workflows, from triggering actions via forms and Application Programming Interfaces (APIs) to incorporating Large Language Models (LLMs) for decision-making and content generation.

Participants will then transition into a 2-day "Bring Your Own Use Case" consultancy model, where they apply their skills to build, refine, and deploy a business solution with guidance from RP lecturers, ensuring the immediate transfer of learning to the workplace.

This course offers two Agentic AI platforms for participants to choose:

• n8n: The "Swiss Army Knife" for builders—a node-based, AI-agnostic platform best for orchestrating complex, API-rich background workflows across any system without vendor limits;

• Microsoft Copilot Studio: The "Corporate Assistant" builder—a low-code platform deeply integrated with Microsoft 365, best for deploying secure, conversational AI agents that live directly in Teams and SharePoint.

Course objectives

Upon completion of this course, participants will be able to:

1. Conceptualize Agentic Workflows

- Differentiate between traditional linear automation and Agentic AI and understand how AI is used within low-code automation platforms to support modern business processes.

2. Architect Intelligent Integrations

- Identify and assemble workflows that connect disparate systems, including Application Programme Interfaces (APIs), databases, communication platforms (e.g., Email, Chat), and AI services, using visual automation tools.

3. Deploy Cognitive Capabilities

- Apply AI technologies such as Natural Language Processing (NLP), sentiment analysis, and generative AI within workflows to manage selected forms of unstructured data.

4. Engineer Robust Logic

- Apply event-based triggers, conditional branching, and Human-in-the-Loop (HITL) mechanisms to improve reliability and governance in automated workflows.

5. Translate Business Needs to Solutions (Project Consultancy Phase)

- Analyse specific organizational pain points to scope, prototype, and refine a practical automation solution during guided project sessions.