AI Agents orchestration with LangGraph: architectures, patterns, and advanced implementation The evolution of LLM-based systems has introduced an important shift: we have moved from applications centered on isolated prompts to coordinated multi-agent systems capable of planning, executing complex tasks, sharing state, and making sequential decisions. In this context, LangGraph emerges as a foundational framework for […]

We know that generative artificial intelligence has transformed the speed of software development. However, generating lines of code faster does not solve old engineering challenges: complexity, lack of standardization, and maintenance difficulties. The promise of creating complex solutions in record time is tempting, but for companies that need stability and security, this speed can hide

AI agents have moved beyond being reactive mechanisms and have become systems capable of navigating complex environments, integrating heterogeneous information, reasoning about dynamic situations, and performing operations that previously required multiple technical teams. This advancement occurs because, behind the scenes, these agents combine data pipelines, language models, memory structures, reasoning heuristics, and execution mechanisms that