A 101 guide to learn agentic AI (thenewaiorder.substack.com)

A comprehensive beginner’s guide from a YC founder breaks down the complex landscape of agentic AI, clarifying key concepts like Retrieval-Augmented Generation (RAG), Multi-Tool Collections (MCPs), prompting, embeddings, and fine-tuning. This guide demystifies how these components work together to empower AI agents beyond just text generation—enabling them to perform real actions like modifying files or querying databases. The explanation highlights how “function calling” allows large language models (LLMs) to invoke external tools and how MCPs act as libraries of these functions, offering modular capabilities without inherently providing context. A central technical insight is the role of RAG, which manages the challenge of feeding relevant context to an LLM by chunking large datasets, creating vector embeddings for efficient search, and retrieving only the most pertinent pieces. This process ensures the agent can work effectively over huge data corpora or codebases without overwhelming the model or incurring excessive computational cost. The guide also distinguishes between training foundational models—resource-intensive and data-heavy—and fine-tuning existing models for specific tasks with smaller datasets, making advanced AI applications more accessible for many organizations. Overall, the article positions agentic AI as an evolving ecosystem of well-orchestrated components, signifying a foundational shift in how AI tools will augment workflows across domains like data analysis, coding, and automation.