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Course Outline

Introduction to Agentic AI

  • Defining agentic capabilities in AI.
  • Key differences between traditional and agentic AI agents.
  • Use cases of agentic AI across 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

  • Foundational 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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