Building effective agents for large language models (LLMs) involves using simple, composable patterns rather than complex frameworks. Successful implementation requires understanding the distinction between workflows and agents, and choosing the appropriate method based on the task's complexity and flexibility needs. While various frameworks like LangGraph and Amazon Bedrock can simplify the development process, it's crucial to understand the underlying code and avoid unnecessary complexity. Practical applications include customer support and coding agents, emphasizing the importance of simplicity, transparency, and well-documented interfaces.
Table of contents
What are agents?When (and when not) to use agentsWhen and how to use frameworksBuilding blocks, workflows, and agentsCombining and customizing these patternsSummaryAppendix 1: Agents in practiceAppendix 2: Prompt engineering your toolsSort: