Create Agentic AI Automations Without Coding

Topic 1: Overview of AI Agents and Agentic AI Workflows

• What is AI Agent and Agentic AI?
• Agentic AI vs Generative AI
• LLM Reasoning Frameworks and their underlying algorithms
• Use Cases of Agentic AI Workflows – APA and Computer Use
• Introduction to Agentic AI platforms – n8n & Langflow
• Analyze the process of creating an AI Agent on Agentic AI Platforms

Covers the Learning Objective 1 – Analyze AI Agents algorithms to identify strengths and limitations in non-coding platform applications.

• Range of AI applications
• Applicability of AI in the Product Development track
• Analyse algorithms in the AI applications
• Identify strengths and limitations of the AI applications

Topic 2: Build Agentic AI Chatbots on No-Code Platforms

• Introduction to AI Chatbot Platforms – Agentx, Promptly & Flowise
• Evaluate the design of an Agentic AI Chatbot on AI Chatbot Platforms
• Introduction to Digital Human Avatar powered by HeyGen and it its core AI algorithms
• Assess the implementation of a Digital Human Avatar Chatbot with No Code

Covers Learning Objective 2 – Analyze AI agent algorithm design for its impact on efficiency and assess AI-driven improvements in product management.

• Algorithm design and implementation
• Methods of evaluating process improvements to product management
• Establish the correlation between design of algorithms and efficiency
• Assess improvements of AI in product

Topic 3: Build Retrieval Augmented Generation

• Overview of Retrieval Augmented Generation (RAG)
• Evaluate the structure and feasibility of Agentic RAG on No-Code Platforms
• Introduction to Agentic Search and Deep Research
• Assess the application design and feasibility of  AI Agents and RAG on Microsoft Copilot Studio

Covers Learning Objective 3 – Evaluate AI Agents application effectiveness and assess feasibility for product and maintenance processes.

• Concepts pertaining to performance effectiveness and analysis
• Methods of evaluating effectiveness of AI applications in products
• Evaluate various AI applications to compare strengths and limitations of the AI applications
• Assess feasibility of AI applications to each product and its’ maintenance processes

This course takes a step-by-step approach to learning and applying agentic AI, starting from core concepts and advancing toward real-world implementation. The learning path is thoughtfully sequenced to ensure each skill builds on the previous one, providing a clear, logical progression.

Unit 1: Introduction to AI Agents and Workflows

• Understand what Agentic AI is, and how it differs from Generative AI.
• Explore key reasoning frameworks that power intelligent agents.
• Analyze use cases across industries and identify where agentic workflows can be applied.
• Create your first AI agent using platforms like n8n and Langflow.

 Unit 2: Developing AI Chatbots

• Dive into no-code chatbot development using Agentx, Promptly, and Flowise.
• Learn how to integrate digital human avatars via HeyGen to create engaging AI experiences.
• Correlate AI algorithm design with efficiency in product management workflows.
• Improve existing agent applications for real-world use cases.

Unit 3: Implementing RAG Applications

• Understand the architecture and benefits of Retrieval Augmented Generation (RAG).
• Build agentic RAG applications to enhance AI reasoning and context retention.
• Leverage Microsoft Copilot Studio to develop intelligent RAG-based search and research agents.
• Evaluate AI’s effectiveness and feasibility for product development and maintenance.

By progressing from foundational theory to hands-on practice and advanced applications, learners are equipped not just with tools, but with strategic insight into building scalable, intelligent systems.

• Executives,
• professionals,
• technicians,
• managers,
• technology leaders,
• CTO,
• CEO,
• HR,
• L&D Managers,
• IT managers