Autonomous Decision-Making with Agentic AI

• 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

Agentic AI refers to artificial intelligence systems capable of autonomous decision-making, self-directed learning, and adaptive responses to dynamic environments.

This instructor-led, live training (online or onsite) is aimed at advanced-level professionals who wish to leverage Agentic AI for decision-making in complex business and technical scenarios.

By the end of this training, participants will be able to:

• Understand the principles of autonomous decision-making in AI.
• Design and implement AI agents that operate with minimal human intervention.
• Integrate Agentic AI into automation workflows and business systems.
• Optimize AI-driven decision processes for efficiency and scalability.
• Ensure compliance, security, and ethical considerations in AI autonomy.

Format of the Course

• Interactive lecture and discussion.
• Lots of exercises and practice.
• Hands-on implementation in a live-lab environment.

Course Customization Options

• To request a customized training for this course, please contact us to arrange.

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