Course Outline

Introduction to Agentic AI and Autonomous Decision-Making

  • What is Agentic AI?
  • Key components of autonomous decision-making
  • Comparing traditional AI and self-governing AI agents

Architectures for Autonomous AI Agents

  • Understanding multi-agent systems
  • Reinforcement learning and decision-making models
  • Designing AI agents for adaptability and self-improvement

Implementing Autonomous AI in Business and Automation

  • Integrating AI agents into enterprise workflows
  • Case studies of AI-powered decision automation
  • Optimizing AI-driven efficiency in business operations

AI Agent Reasoning and Planning

  • Knowledge-based decision-making models
  • Goal-oriented reasoning and action selection
  • Handling uncertainty in autonomous AI

Optimizing AI Decision Processes

  • Scaling autonomous AI for real-world applications
  • AI performance tuning for complex decision environments
  • Minimizing bias and improving AI-driven outcomes

Security, Compliance, and Ethical Considerations

  • Ensuring AI safety in autonomous decision-making
  • Regulatory frameworks and compliance
  • Best practices for responsible AI use

Future of Autonomous AI and Decision-Making

  • Trends in self-learning AI agents
  • Emerging technologies in autonomous decision systems
  • Expanding Agentic AI applications in various industries

Summary and Next Steps

Requirements

  • Experience with AI-driven automation
  • Familiarity with reinforcement learning and decision-making models
  • Understanding of AI agent architectures

Audience

  • AI developers designing autonomous decision-making systems
  • Automation specialists integrating AI agents into workflows
  • Business analysts optimizing decision-making with AI
 14 Hours

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Price per participant

Provisional Upcoming Courses (Require 5+ participants)

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