Core Concepts of Agentic AI
This training introduces the definition of Agentic AI, its components (planning, memory, tool use, reflection), common agent architectures (e.g., ReAct), and use cases. Focuses on conceptual understanding and simple agent patterns.
Pre-Requisite
- Introduction to AI & LLMs, or equivalent foundational AI knowledge.
Course Outline
Curriculum
- 3 Sections
- 17 Lessons
- 3 Days
Expand all sectionsCollapse all sections
- DAY 1 - What are AI Agents?6
- 1.1Defining Agentic AI (Autonomy, Goal-orientation, Reactivity, Proactiveness)
- 1.2The Agentic Loop (Perceive, Act, Reflect, Plan)
- 1.3Differences between Traditional AI/LLMs and Agentic AI
- 1.4Key Components of an AI Agent (Planning, Memory, Tool Use, Reflection)
- 1.5Simple Agent Architectures (e.g., ReAct, Plan-and-Execute)
- 1.6Case Studies: Real-world examples of simple agents
- DAY 2 - Planning & Reasoning in Agents5
- DAY 3 - Introduction to Agent Capabilities6
- 3.1Overview of Tool Use (Agents interacting with external systems)
- 3.2Basic Memory Concepts (Short-term context, history)
- 3.3Introduction to Reflection/Self-Correction
- 3.4Agent Benchmarking and Evaluation Metrics (Basic understanding)
- 3.5Discussion: Opportunities and Challenges of Agentic AI
- 3.6Mini-Project: Outline a basic agent design for a simple task.
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