Course Outline

Introduction to Agentic AI

  • Defining agentic capabilities in AI
  • Key differences between traditional and agentic AI agents
  • Use cases of agentic AI in various industries

Developing Goal-Driven AI Agents

  • Understanding autonomous goal-setting and prioritization
  • Implementing reinforcement learning for self-improvement
  • Fine-tuning AI agent behaviors based on feedback loops

Multi-Agent Collaboration and Coordination

  • Building AI agents that collaborate and communicate
  • Task delegation and role assignment in agentic systems
  • Real-world examples of multi-agent teamwork

Adaptive AI-Human Interaction

  • Personalizing AI responses based on user behavior
  • Context-awareness and dynamic decision-making
  • Designing UX for intelligent and responsive AI agents

Deploying Agentic AI in Applications

  • Integrating agentic AI with APIs and third-party tools
  • Ensuring scalability and efficiency in AI deployments
  • Case studies on successful agentic AI implementations

Ethical Considerations and Challenges

  • Balancing autonomy with control in AI agents
  • Addressing AI biases and ethical concerns
  • Regulatory frameworks for autonomous AI systems

Future Trends in Agentic AI

  • Emerging advancements in AI autonomy
  • Expanding agentic capabilities with new technologies
  • Predictions for AI-driven automation and decision-making

Summary and Next Steps

Requirements

  • Basic knowledge of AI agents and automation
  • Experience with Python programming
  • Understanding of API-based AI integrations

Audience

  • AI developers enhancing autonomous systems
  • Automation engineers optimizing AI-driven workflows
  • UX designers improving human-agent interactions
 14 Hours

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