Agentic AI in Multi-Agent Systems

• Experience with AI model development
• Understanding of multi-agent system concepts
• Familiarity with reinforcement learning and AI-driven automation

Audience

• AI researchers studying autonomous agent interactions
• Robotics engineers designing multi-agent coordination
• Game developers implementing AI-driven NPC behavior

Agentic AI plays a key role in multi-agent systems, enabling autonomous agents to interact, collaborate, and make decisions in complex environments.

This instructor-led, live training (online or onsite) is aimed at advanced-level professionals who wish to develop and optimize multi-agent systems using Agentic AI frameworks.

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

• Understand the principles of Agentic AI in multi-agent environments.
• Develop AI-driven agents that interact autonomously.
• Implement reinforcement learning for adaptive AI behavior.
• Optimize multi-agent collaboration and competition.
• Apply Agentic AI in robotics, gaming, and enterprise automation.

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 Multi-Agent Systems

• Defining multi-agent systems and their applications
• Role of Agentic AI in autonomous agent interactions
• Challenges in multi-agent coordination

Developing Agentic AI for Multi-Agent Environments

• Designing autonomous AI agents
• Agent communication and decision-making strategies
• Simulation environments for multi-agent AI

Reinforcement Learning for Agentic AI

• Applying reinforcement learning to multi-agent systems
• Training autonomous agents for adaptive behavior
• Balancing exploration and exploitation in decision-making

Collaboration and Competition in Multi-Agent Systems

• Cooperative AI agent strategies
• Competitive and adversarial AI interactions
• Emergent behaviors in multi-agent environments

Agentic AI in Robotics and Automation

• Multi-agent coordination in robotics
• Swarm intelligence and decentralized decision-making
• Case studies in robotic AI applications

Agentic AI in Game Development

• Designing AI-driven NPCs in multi-agent simulations
• Behavior modeling for interactive AI agents
• Real-time AI decision-making in dynamic environments

Scaling Multi-Agent AI Systems

• Performance optimization for large-scale AI interactions
• Managing agent hierarchies and role-based decision-making
• Integrating AI agents with cloud-based environments

Future of Multi-Agent Systems with Agentic AI

• Emerging trends in autonomous AI collaboration
• Expanding multi-agent AI capabilities with deep learning
• Ethical and regulatory considerations for multi-agent AI

Summary and Next Steps