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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
