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Course Outline
Introduction to AI Agents
- Defining the concept of AI agents.
- Classification of AI agents: Reactive, proactive, and hybrid models.
- Real-world applications of AI agents.
Foundational Design Principles
- Essential components of an AI agent.
- The interaction dynamics between agents and their environment.
- An introduction to agent-based modeling.
Developing Simple AI Agents
- A survey of tools and frameworks for AI agent creation.
- Practical session: Building a basic chatbot with Rasa.
- Modifying agent behavior configurations.
Advanced AI Agent Features
- Integrating natural language understanding capabilities.
- Incorporating machine learning models.
- Customizing agent responses for personalization.
Practical Applications
- Implementing AI agents in customer service operations.
- Utilizing virtual assistants for personal productivity.
- Deploying interactive educational tools.
Optimizing Performance
- Improving agent efficiency.
- Addressing scalability factors.
- Evaluating agent effectiveness through KPIs.
Ethical and Societal Impact
- Mitigating biases within AI agents.
- Safeguarding privacy and data security.
- Adhering to AI regulatory standards.
Challenges and Future Trajectories
- Overcoming scalability and performance constraints.
- Navigating ethical considerations in AI agent deployment.
- Monitoring emerging trends in AI agent technology.
Requirements
- A foundational grasp of artificial intelligence concepts
- Basic proficiency in Python programming
Target Audience
- Individuals with a keen interest in AI
- Professionals within the IT sector
14 Hours
