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Course Outline
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
Requirements
- 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
14 Hours
