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Course Outline

Introduction to Agentic AI Systems

  • Defining Agentic AI and its core capabilities
  • Key distinctions between rule-based AI and autonomous AI
  • Real-world use cases and industry applications

Architecting Agentic AI Systems

  • Frameworks and tools for building autonomous AI
  • Designing AI agents with goal-oriented functionalities
  • Implementing memory, context awareness, and adaptability

Developing AI Agents with Python and APIs

  • Constructing AI agents
  • Integrating AI models with external data sources
  • Processing API responses and enhancing agent interactions

Optimizing Multi-Agent Collaboration

  • Designing AI agents for both cooperative and competitive tasks
  • Managing agent communication and task delegation
  • Scaling multi-agent systems for real-world deployment

Enhancing Decision-Making in Agentic AI

  • Reinforcement learning and self-improving AI agents
  • Planning, reasoning, and executing long-term objectives
  • Balancing automation with human oversight

Security, Ethics, and Compliance in Agentic AI

  • Addressing biases and ensuring responsible AI deployment
  • Security measures for AI-driven decision-making
  • Regulatory considerations for autonomous AI systems

Future Trends in Agentic AI

  • Advancements in AI autonomy and self-learning systems
  • Expanding AI agent capabilities with multimodal learning
  • Preparing for the next generation of autonomous AI

Summary and Next Steps

Requirements

  • Foundational understanding of AI and machine learning concepts
  • Proficiency in Python programming
  • Familiarity with integrating AI models via APIs

Target Audience

  • AI engineers engaged in the development of autonomous AI systems
  • ML researchers investigating multi-agent AI frameworks
  • Developers working on AI-driven automation implementations
 21 Hours

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