Generative AI and Agentic AI

• An understanding of AI and machine learning concepts
• Experience working with APIs or scripting languages such as Python
• Familiarity with prompt engineering or large language model usage

Audience

• AI developers and engineers
• Innovation and R&D teams
• Technical product managers exploring agentic AI systems

Generative AI and Agentic AI are two powerful paradigms driving the next wave of automation and intelligence — one focused on content generation and the other on goal-driven, autonomous behavior.

This instructor-led, live training (online or onsite) is aimed at intermediate-level AI and technical professionals who wish to understand how to build, evaluate, and integrate generative and agentic AI into real-world applications.

By the end of this training, participants will be able to:

• Understand the architecture and capabilities of generative AI systems.
• Explore the rise of autonomous AI agents and how they extend LLMs.
• Use prompt engineering and tool integrations for practical deployments.
• Compare models, tools, and techniques for responsible deployment.

Format of the Course

• Interactive lecture and discussion.
• Hands-on use of generative and agentic AI tools in real-world scenarios.
• Guided exercises focused on content generation and autonomous workflows.

Course Customization Options

• To request a customized training for this course, please contact us to arrange.

Introduction to Generative AI and Agentic AI

• What is Generative AI? What is Agentic AI?
• How they differ and complement each other
• Use cases and trends across industries

Generative AI Architecture and Tools

• Transformer models: GPT, LLaMA, Claude, and others
• Fine-tuning vs. in-context learning
• Tools: ChatGPT, Hugging Face Transformers, Google AI Studio

Prompt Engineering for Control and Structure

• Prompt patterns for writing, coding, summarization, etc.
• Few-shot, zero-shot, and chain-of-thought prompting
• Using prompt libraries and testing tools

Understanding Agentic AI

• Definition and evolution of agentic AI
• Architectures: planning, memory, tools, self-reflection
• Popular frameworks: AutoGPT, BabyAGI, CrewAI, LangGraph

Designing and Deploying Autonomous Agents

• Goal setting and task decomposition
• Integrating tools and APIs (search, memory, code)
• Multi-agent coordination and human-in-the-loop supervision

Use Cases and Implementation Scenarios

• Content generation vs. task orchestration
• Enterprise productivity, customer support, data extraction
• Responsible and secure implementation

Summary and Next Steps