Agentic AI Mastery

Agentic AI Mastery

From Prompt to Autonomous Execution in 2 days

In 2025, agentic AI has evolved from a futuristic concept to a practical tool reshaping industries. These AI agents, capable of autonomous decision-making and task execution, are revolutionizing workflows across sectors. This two-day intensive course is designed to bridge the gap between non-technical professionals and developers, providing a comprehensive understanding of agentic AI.

Day 1 focuses on non-technical users, introducing them to the capabilities of agentic AI through hands-on experience with tools like OpenAI's Operator, Deep Research, and Manus AI. Day 2 delves into the technical aspects, guiding developers through building custom AI agents using LangChain, Python, and large language models (LLMs).

Led by an instructor with over 30 years of industry experience, this course emphasizes real-world applications, ensuring participants can immediately apply their knowledge to enhance productivity and innovation.

Learning Outcomes

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

• Day 1 (Non-Technical):
  • Understand the fundamentals of agentic AI and its applications.
  • Utilize tools like OpenAI's Operator and Deep Research for task automation and research.
  • Leverage Manus AI for autonomous task execution.
  • Apply prompt engineering techniques to guide AI behavior effectively.
• Day 2 (Technical):
  • Comprehend the architecture of AI agents and their integration with LLMs.
  • Develop custom AI agents using LangChain and Python.
  • Implement APIs and tool integrations for enhanced agent capabilities.
  • Address ethical considerations and best practices in deploying AI agents.

Prerequisites

• Day 1:
  • Basic computer literacy.
  • Familiarity with web browsing and common software applications.
• Day 2:
  • Proficiency in Python programming.
  • Understanding of APIs and web development concepts.
  • Experience with LLMs and AI frameworks is beneficial.

Detailed Training Outline

Day 1: Agentic AI for Non-Technical Users

1. Introduction to Agentic AI

• Definition and evolution of agentic AI.
• Real-world applications and case studies.
• Overview of key tools: Operator, Deep Research, Manus AI.

2. OpenAI's Operator

• Understanding Operator's capabilities in automating web tasks.
• Hands-on exercises: Filling forms, scheduling appointments, and online shopping automation.
• Best practices for using Operator effectively.

3. Deep Research by OpenAI

• Exploring Deep Research for comprehensive information gathering.
• Creating detailed reports with citations and summaries.
• Integrating Deep Research outputs into decision-making processes.

4. Manus AI

• Introduction to Manus AI's autonomous task execution.
• Demonstrations: Website creation, data analysis, and content generation.
• Comparative analysis with other AI agents.

5. Prompt Engineering Essentials

• Crafting effective prompts to guide AI behavior.
• Understanding the impact of prompt structure on outcomes.
• Interactive session: Refining prompts for desired results.

6. Practical Applications and Use Cases

• Identifying opportunities for agentic AI in various industries.
• Developing strategies to integrate AI agents into daily workflows.
• Group discussions: Sharing ideas and experiences.

Day 2: Building AI Agents with LangChain and Python

1. Deep Dive into Agentic AI Architecture

• Understanding the components of AI agents: LLMs, tools, memory, and planning.
• Exploring the ReAct pattern for decision-making.
• Analyzing the flow of information and actions within an agent.(

2. Introduction to LangChain

• Setting up the LangChain environment.
• Creating simple chains and understanding their execution.
• Utilizing LangChain's built-in tools and integrations.

3. Developing Custom Agents

• Defining agent behavior and goals.
• Integrating external APIs and tools for enhanced capabilities.
• Implementing memory and context management.
• Error handling and fallback strategies.

4. Advanced Agent Features

• Incorporating real-time data retrieval and processing.
• Enabling multi-step reasoning and planning.
• Deploying agents for specific tasks: Data analysis, content generation, and automation.

5. Ethical Considerations and Best Practices

• Addressing biases and ensuring fairness in AI outputs.
• Implementing security measures and data privacy protocols.
• Establishing guidelines for responsible AI agent deployment.

6. Hands-On Project

• Participants will design and develop a custom AI agent tailored to a specific use case.
• Presentations and peer reviews to provide feedback and insights.

This comprehensive course offers a unique blend of theoretical knowledge and practical experience, empowering participants to harness the full potential of agentic AI in their respective domains.