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Course Outline
Introduction to Agentic AI
- Defining agentic AI and its connection to conventional AI systems
- An overview of reasoning, memory, and goal-oriented architectures
- Key applications across various industries
Core Concepts and Design Patterns
- The agent cycle: perception, reasoning, and execution
- Differentiating between single-agent and multi-agent setups
- Interacting with environments and calling tools
Essentials of Prompt Engineering
- Crafting prompts that enhance reasoning and task breakdown
- Leveraging examples, constraints, and role definitions for better control
- Systematically debugging and refining prompts
Constructing Basic Agentic Workflows
- Building an agent loop using Python
- Connecting to APIs and basic utility tools
- Managing agent state and memory structures
Responsible Design and Safety Protocols
- Ethical implications and best practices for agent usage
- Addressing bias, transparency, and accountability in AI
- Implementing access controls, data security, and content safety measures
Practical Project: Creating a Responsible Agent
- Setting the problem boundaries and goals
- Writing prompts and control logic
- Testing, adjusting, and assessing agent performance
Requirements
- Fundamental grasp of AI or machine learning principles
- Comfort with Python syntax and scripting
- Experience handling data or API-driven applications
Target Audience
- Data scientists beginning their journey in agentic AI development
- Junior ML engineers investigating applied agent architectures
- Tech managers looking to comprehend agent design and safety standards
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
