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Course Outline
Basics of AI Agents
- Defining AI agents
- Classifications: Reactive, proactive, and hybrid models
- Real-world implementation scenarios
Core Architectural Principles
- Essential components of an AI agent
- Interaction dynamics between agents and their environment
- Introduction to agent-based modeling techniques
Developing Elementary AI Agents
- Survey of development tools and frameworks
- Practical exercise: Building a basic chatbot with Rasa
- Tailoring agent behavioral responses
Enhanced Agent Functionality
- Integration of natural language comprehension
- Embedding machine learning algorithms
- Customizing responses for personalization
Applied Use Cases
- AI agents in client support services
- Virtual assistants and productivity enhancement tools
- Interactive learning platforms
Efficiency and Performance Tuning
- Improving operational efficiency
- Strategies for scalability
- Evaluating success through Key Performance Indicators
Etical and Societal Impact
- Mitigating biases within AI systems
- Safeguarding privacy and data integrity
- Adhering to AI governance regulations
Current Challenges and Future Trajectories
- Limitations in scale and performance
- Ethical frameworks for deployment
- Emerging trends in AI agent innovation
Requirements
- Fundamental knowledge of artificial intelligence principles
- Working proficiency in Python programming
Target Audience
- Individuals passionate about AI
- Information Technology specialists
14 Hours
