Building Effective AI Agents with Large Language Models: Workflows, Design Patterns, and Best Practices

Sunil Karthik Kota

Abstract: Recent advancements in Large Language Models (LLMs) have significantly enhanced the development of AI agents capable of sophisticated natural language understanding and decision - making. This article explores key methodologies, workflows, and design patterns for building effective AI agents using LLMs. By examining techniques such as prompt chaining, routing, parallelization, and orchestrator - worker models alongside design patterns like reflection, planning, tool use, and multi - agent collaboration, we outline a structured approach for creating robust, autonomous systems. In addition, we discuss best practices to ensure transparency, optimize performance, and address both security and ethical considerations. These guidelines are essential as AI agents become integrated into an increasingly wide range of applications, from customer service to complex research and development tasks.

Keywords: AI Agents, Large Language Models (LLMs), Prompt Chaining, Routing, Parallelization, Orchestrator - Worker, Reflection, Planning, Tool Use, Multi - Agent Collaboration, Security and Ethics