Foundations
Build a solid foundation in agentic AI. Learn what AI agents are, how they differ from traditional AI, and the core concepts of agency.
🎯What You'll Learn
- ✓What defines an AI agent
- ✓Agent architectures: reactive, deliberative, hybrid
- ✓Perception-action loops and goal-oriented behavior
- ✓Agent vs traditional AI systems
- ✓Agent environments and interactions
💪Skills You'll Gain
🏆Learning Outcomes
📖Interactive Modules (10)
What is Agentic AI?
Introduction to agentic AI, autonomous systems that perceive, reason, and act to achieve goals.
Agents vs Simple LLM Apps
Understand the difference between simple LLM applications and autonomous AI agents.
Evolution of AI Agents
Trace the evolution of AI agents from rule-based systems to modern LLM-powered agents.
Anatomy of an Agent
Explore the core components of AI agents: perception, reasoning, memory, and action.
Core Agent Capabilities
Learn what AI agents can do: tool use, planning, learning, and adaptation.
Current Limitations
Understand current limitations of agentic AI: hallucination, reliability, and safety challenges.
Key Terminology & Concepts
Master key terminology: prompts, context, tools, memory, planning, and orchestration.
The Agentic AI Ecosystem
Navigate the agentic AI ecosystem: frameworks, platforms, tools, and communities.
Use Cases Overview
Explore real-world applications: customer service, research, coding, data analysis, automation.
Your First Agent Demo
Build and interact with your first AI agent in an interactive demonstration.