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Duration 14 hours
Course Outline
Introduction to Agentic AI and Autonomous Decision-Making
- Defining Agentic AI
- Essential elements of autonomous decision-making
- Contrasting traditional AI with self-governing AI agents
Architectures for Autonomous AI Agents
- Exploring multi-agent systems
- Reinforcement learning techniques and decision models
- Building AI agents focused on adaptability and self-improvement
Implementing Autonomous AI in Business and Automation
- Embedding AI agents within enterprise workflows
- Analyzing case studies of AI-powered decision automation
- Enhancing operational efficiency through AI-driven processes
AI Agent Reasoning and Planning
- Models for knowledge-based decision-making
- Goal-oriented reasoning and strategic action selection
- Managing uncertainty in autonomous AI systems
Optimizing AI Decision Processes
- Scaling autonomous AI for practical, real-world use
- Tuning AI performance in complex decision environments
- Reducing bias and elevating AI-driven outcomes
Security, Compliance, and Ethical Considerations
- Safeguarding AI safety in autonomous decision-making
- Navigating regulatory frameworks and compliance requirements
- Adopting best practices for responsible AI utilization
The Future of Autonomous AI and Decision-Making
- Emerging trends in self-learning AI agents
- New technologies shaping autonomous decision systems
- Broadening the scope of Agentic AI across diverse industries
Summary and Next Steps
Requirements
- Experience with AI-driven automation processes
- Working knowledge of reinforcement learning and decision-making models
- Conceptual understanding of AI agent architectures
Target Audience
- AI developers building autonomous decision-making systems
- Automation specialists incorporating AI agents into operational workflows
- Business analysts leveraging AI to optimize decision-making
Testimonials (1)
The mix of theory and practice and of high level and low level perspectives
