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Course Outline
Foundations of AI Agents
- Defining the concept of AI agents.
- Categorizing agents: Reactive, proactive, and hybrid models.
- Real-world implementation scenarios for AI agents.
Core Design Principles
- Essential components that constitute an AI agent.
- Dynamics of interaction between agents and their environments.
- Overview of agent-based modeling techniques.
Developing Simple AI Agents
- Survey of tools and frameworks for AI agent creation.
- Practical session: Building a basic chatbot with Rasa.
- Modifying and customizing agent behaviors.
Advanced Capabilities
- Integrating natural language understanding capabilities.
- Incorporating machine learning models into agent logic.
- Personalizing agent responses for user experience.
Practical Applications
- Deploying AI agents in customer service operations.
- Virtual assistants and personal productivity enhancements.
- Interactive solutions for educational purposes.
Optimizing Performance
- Strategies for improving agent efficiency.
- Considerations for system scalability.
- Evaluating agent performance through Key Performance Indicators (KPIs).
Ethical and Societal Impact
- Mitigating biases inherent in AI agents.
- Safeguarding privacy and data security.
- Navigating regulatory compliance for AI.
Challenges and Future Trajectories
- Overcoming limitations in scalability and performance.
- Ethical frameworks for deploying AI agents.
- Emerging trends shaping the future of AI agent technology.
Requirements
- Fundamental knowledge of artificial intelligence principles.
- Proficiency in Python programming.
Target Audience
- Individuals with an interest in AI.
- IT professionals.
14 Hours
