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Course Outline
Introduction to Agentic AI
- Defining agentic AI and contrasting it with conventional AI systems
- Insight into reasoning, memory, and goal-oriented structures
- Primary use cases and sector-specific applications
Key Concepts and Architectural Patterns
- The agent cycle: sensing, reasoning, and executing actions
- Distinguishing between single-agent and multi-agent ecosystems
- Interacting with environments and invoking tools
Basics of Prompt Engineering
- Crafting effective prompts for logical reasoning and task breakdown
- Leveraging examples, constraints, and role definitions for enhanced control
- Systematically debugging and refining prompts
Constructing Basic Agentic Workflows
- Building an agent loop in Python
- Connecting with APIs and utility tools
- Overseeing agent state and memory management
Ethical Design and Safety Standards
- Moral implications and responsible agent deployment
- Addressing bias, transparency, and accountability in AI
- Managing access controls, data privacy, and content safety
Practical Project: Creating an Ethical Agent
- Establishing the problem domain and goals
- Creating prompts and control mechanisms
- Validating, optimizing, and assessing agent performance
Requirements
- Foundational knowledge of AI or machine learning principles
- Proficiency with Python syntax and scripting
- Background in data handling or API-driven applications
Target Audience
- Data scientists beginning their journey in agentic AI
- Entry-level ML engineers investigating practical agent architectures
- Technical leaders aiming 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
