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Course Outline
Introduction to Agentic AI
- Defining agentic AI and its connection to conventional AI systems
- Exploring reasoning, memory, and goal-oriented architectures
- Identifying key use cases and industrial applications
Fundamental Concepts and Design Patterns
- The agent cycle: perception, reasoning, and execution
- Comparing single-agent and multi-agent systems
- Interacting with environments and invoking tools
Basics of Prompt Engineering
- Crafting effective prompts for reasoning and task breakdown
- Leveraging examples, constraints, and roles for improved control
- Systematically debugging and refining prompts
Constructing Basic Agentic Workflows
- Building an agent loop using Python
- Connecting with APIs and basic tools
- Overseeing agent state and memory management
Ethical Design and Safety Protocols
- Ethical implications and responsible deployment of agents
- Addressing bias, transparency, and accountability in AI systems
- Implementing access control, data security, and content safety
Practical Project: Creating a Responsible Agent
- Establishing the problem scope and goals
- Developing prompt structures and control logic
- Testing, optimizing, and assessing agent performance
Requirements
- A foundational grasp of AI or machine learning concepts
- Proficiency in Python syntax and scripting
- Experience with data handling or API-driven applications
Target Audience
- Data scientists new to the field of agentic AI development
- Junior ML engineers interested in exploring applied agent architectures
- Technology managers looking to comprehend agent design and safety principles
14 Hours
Testimonials (3)
The trainer is patient and very helpful. He knows the topic well.
CLIFFORD TABARES - Universal Leaf Philippines, Inc.
Course - Agentic AI for Business Automation: Use Cases & Integration
Good mixvof knowledge and practice
Ion Mironescu - Facultatea S.A.I.A.P.M.
Course - Agentic AI for Enterprise Applications
The mix of theory and practice and of high level and low level perspectives
