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Course Outline
Overview of Agentic AI
- Defining agentic AI and its distinction from traditional AI systems
- Summary of reasoning, memory, and goal-oriented architectures
- Primary use cases and sector-specific applications
Key Principles and Design Patterns
- The agent cycle: perception, reasoning, and action
- Comparison of single-agent and multi-agent systems
- Interaction with environments and tool usage
Essentials of Prompt Engineering
- Crafting effective prompts for reasoning and breaking down tasks
- Leveraging examples, constraints, and roles for enhanced control
- Systematic debugging and refinement of prompts
Creating Basic Agentic Workflows
- Executing an agent loop in 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
- Bias, transparency, and accountability within AI systems
- Access control, data security, and content safety measures
Practical Project: Developing a Responsible Agent
- Establishing problem scope and goals
- Constructing the prompt 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
- Practical experience with data processing or API-based applications
Target Audience
- Data scientists entering the field of agentic AI development
- Junior ML engineers investigating practical agent architectures
- Technology leaders 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
