Building Agentic AI Systems: From Theory to Practice

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