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Course Outline
Introduction to Agentic AI
- Defining agentic AI and its connection to traditional AI systems
- Review of reasoning, memory, and goal-driven architectures
- Major use cases and industry applications
Core Concepts and Design Patterns
- The agent loop: perception, reasoning, and action
- Comparing single-agent and multi-agent systems
- Interaction with environments and tool invocation
Prompt Engineering Fundamentals
- Crafting effective prompts for reasoning and task breakdown
- Leveraging examples, constraints, and roles for enhanced control
- Systematic debugging and iterative refinement of prompts
Building Simple Agentic Workflows
- Implementing an agent loop in Python
- Integration with APIs and basic tools
- Managing agent state and memory
Responsible Design and Safety Practices
- Ethical considerations and the responsible use of agents
- Addressing bias, transparency, and accountability in AI systems
- Implementing access control, data protection, and content safety
Hands-on Project: Designing a Responsible Agent
- Defining the problem scope and objectives
- Developing prompts and control logic
- Testing, refining, and evaluating agent behavior
Requirements
- A foundational grasp of AI or machine learning concepts
- Proficiency with Python syntax and scripting
- Experience handling data or developing API-based applications
Target Audience
- Data scientists new to agentic AI development
- Junior ML engineers exploring applied agent architectures
- Technology managers looking to understand 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
