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Course Outline
Introduction to Agentic AI
- Defining agentic AI and distinguishing it from conventional AI systems
- Overview of reasoning capabilities, memory structures, and goal-oriented architectures
- Key application scenarios and industry-specific use cases
Fundamental Concepts and Architectural Patterns
- The agent loop: processes of perception, reasoning, and action
- Comparison between single-agent and multi-agent system configurations
- Mechanisms for environment interaction and tool invocation
Foundations of Prompt Engineering
- Creating effective prompts tailored for reasoning and task breakdown
- Leveraging examples, constraints, and role assignments to enhance control
- Systematic approaches to debugging and refining prompts
Developing Basic Agentic Workflows
- Implementing an agent loop using Python
- Connecting agents to external APIs and utility tools
- Techniques for managing agent state and maintaining memory
Ethical Design and Safety Protocols
- Ethical implications and responsible usage of AI agents
- Addressing bias, ensuring transparency, and establishing accountability in AI systems
- Implementing access controls, data protection, and content safety measures
Practical Project: Engineering a Responsible Agent
- Establishing problem scope and defining clear objectives
- Developing the prompt structure and control logic
- Conducting tests, refining logic, and evaluating agent performance
Requirements
- Fundamental grasp of AI or machine learning principles
- Proficiency in Python syntax and basic scripting
- Practical experience handling data or interacting with API-driven applications
Target Audience
- Data scientists looking to enter the field of agentic AI development
- Junior ML engineers seeking to explore applied agent architectures
- Technology leaders aiming to comprehend agent design standards 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
