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Course Outline
Introduction to Agentic AI
- Defining agentic AI and its distinction from traditional AI systems
- An overview of reasoning, memory, and goal-driven architectures
- Key use cases and applications across various industries
Core Concepts and Design Patterns
- The agent loop: perception, reasoning, and action
- Comparing single-agent and multi-agent systems
- Interactions with the environment and tool invocation
Prompt Engineering Fundamentals
- Crafting effective prompts for reasoning and task decomposition
- Leveraging examples, constraints, and roles for enhanced control
- Systematically debugging and iterating on prompts
Building Simple Agentic Workflows
- Implementing an agent loop using Python
- Integrating 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
- Foundational understanding of AI or machine learning concepts
- Proficiency with Python syntax and scripting
- Practical experience with data or API-based applications
Target Audience
- Data scientists beginning their journey in agentic AI development
- Junior ML engineers investigating 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
