Understand the core building blocks of AI agents, including how Large Language Models (LLMs) function, the role of transformers in processing and generating human-like text, and the principles behind agentic workflows. This section covers how agents autonomously complete tasks by reasoning, planning, and interacting with environments.

Learn how AI agents can solve complex problems by breaking them into smaller, logical steps using Chain-of-Thought (CoT) prompting. Explore state tracking techniques that allow agents to remember and adjust their responses based on previous interactions, ensuring continuity and coherence in multi-step problem-solving.

Delve into how AI agents store, retrieve, and utilize past knowledge efficiently. Understand the importance of vector databases in storing high-dimensional embeddings and how Retrieval-Augmented Generation (RAG) enhances LLM responses by fetching relevant information from external sources, ensuring more accurate and context-aware answers.

Master the techniques for integrating external tools and APIs into AI agent workflows. Learn how to make API calls to fetch real-time data, interact with third-party services, and implement Reinforcement Learning (RL) basics to help AI agents improve their decision-making capabilities over time.

Go beyond standard prompting techniques with role prompting, where AI agents assume specific personas to enhance response quality. Understand self-improving loops, where agents refine their outputs iteratively, learning from feedback and optimizing their performance without human intervention.

Learn how AI agents interact with websites by automating browser tasks using Playwright and Selenium. Gain hands-on experience in DOM parsing, handling JavaScript-rendered content, and enabling dynamic interactions such as form submissions, button clicks, and data extraction.

Discover how multiple AI agents can collaborate to solve tasks more efficiently. Understand the principles of agent orchestration, where different agents specialize in various subtasks and communicate seamlessly. Learn best practices for error handling to ensure robustness in complex, multi-agent systems.