Building Agentic AI Systems: From Theory to Practice

• Basic understanding of AI and machine learning concepts
• Experience with Python programming
• Familiarity with API-based AI model integration

Audience

• AI engineers developing autonomous AI systems
• ML researchers exploring multi-agent AI frameworks
• Developers implementing AI-powered automation

Agentic AI systems are capable of autonomous decision-making, self-improvement, and multi-agent collaboration.

This instructor-led, live training (online or onsite) is aimed at intermediate-level AI engineers, ML researchers, and developers who wish to build and implement Agentic AI systems in real-world applications.

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

• Understand the core principles of Agentic AI systems.
• Implement AI agents capable of autonomous reasoning and action.
• Integrate Agentic AI with APIs and third-party services.
• Optimize multi-agent interactions for complex tasks.
• Address ethical, security, and scalability challenges in Agentic AI.

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 Systems

• Defining Agentic AI and its capabilities
• Key differences between rule-based AI and autonomous AI
• Use cases and industry applications

Architecting Agentic AI Systems

• Frameworks and tools for building autonomous AI
• Designing AI agents with goal-driven capabilities
• Implementing memory, context-awareness, and adaptability

Developing AI Agents with Python and APIs

• Building AI agents using OpenAI and DeepSeek APIs
• Integrating AI models with external data sources
• Handling API responses and improving agent interactions

Optimizing Multi-Agent Collaboration

• Designing AI agents for cooperative and competitive tasks
• Managing agent communication and task delegation
• Scaling multi-agent systems for real-world applications

Enhancing Decision-Making in Agentic AI

• Reinforcement learning and self-improving AI agents
• Planning, reasoning, and long-term goal execution
• Balancing automation with human oversight

Security, Ethics, and Compliance in Agentic AI

• Addressing biases and ensuring responsible AI deployment
• Security measures for AI-driven decision-making
• Regulatory considerations for autonomous AI systems

Future Trends in Agentic AI

• Advancements in AI autonomy and self-learning systems
• Expanding AI agent capabilities with multimodal learning
• Preparing for the next generation of autonomous AI

Summary and Next Steps