What you need to make an agent

This article delves into creating autonomous agents for internal tooling and processes, focusing on scaling without manual work. It references the paper 'From Language to Action' and outlines core components: RAG (Retrieval Augmented Generation), tool calling, memory management, and feedback loops. It demonstrates using Anthropic's Python SDK for agent creation, explains how RAG grounds LLM responses in real data, and details tool calling for dynamic retrieval. Memory techniques like session summarization are discussed to manage prompt size. Practical examples include database queries and order tracking.