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Course Outline

Getting Started with Agentic AI

  • Defining agentic AI and its distinction from conventional AI systems
  • An overview of reasoning, memory, and goal-oriented architectures
  • Key use cases and applications across various industries

Fundamental Concepts and Architectural Patterns

  • The agent cycle: perception, reasoning, and execution
  • Comparing single-agent and multi-agent configurations
  • Interaction with environments and invoking external tools

Basics of Prompt Engineering

  • Crafting prompts that facilitate reasoning and task breakdown
  • Leveraging examples, constraints, and role definitions for enhanced control
  • Systematically debugging and refining prompts

Constructing Basic Agentic Workflows

  • Implementing an agent loop using Python
  • Connecting with APIs and utilising simple tools
  • Handling agent state and memory management

Ethical Design and Safety Protocols

  • Ethical implications and responsible deployment of agents
  • Addressing bias, ensuring transparency, and maintaining accountability
  • Managing access control, data privacy, and content safety

Practical Project: Creating a Responsible Agent

  • Establishing problem scope and project objectives
  • Writing prompts and defining control logic
  • Testing, iterating, and assessing agent performance

Requirements

  • A foundational grasp of AI or machine learning concepts
  • Proficiency with Python syntax and scripting
  • Practical experience with data handling or API-driven applications

Target Audience

  • Data scientists beginning their journey in agentic AI development
  • Junior ML engineers investigating practical agent architectures
  • Technology leaders looking to comprehend agent design and safety standards
 14 Hours

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