As AI systems evolve from conversational assistants into tool-driven and action-oriented platforms, a standardized way to connect Large Language Models (LLMs) with backend capabilities becomes essential. Directly coupling prompts with APIs leads to tight dependencies, security risks, and poor scalability.

When building AI agents with Large Language Models (LLMs), there are several proven patterns that help create more intelligent and reliable systems. LangGraph provides excellent tools to implement these patterns. Let's explore the most common agentic workflows that you can use to build powerful AI applications.

Artificial Intelligence (AI) is rapidly reshaping how we build and interact with software in our daily lives. From generating human-like text, images and audio, AI models, particularly large language models (LLMs), are now core components of modern digital systems and software.

Artificial Intelligence (AI) has become a cornerstone of modern software development, enabling applications to process natural language, recognize images, and make predictions with unprecedented accuracy.