Stop Building Brittle Agents: Master the ReAct Pattern for Complex SaaS Tasks

Are you building AI agents that seem smart in a demo but fall apart with real-world complexity? You ask them to perform a multi-step task, and they either hallucinate an answer or get stuck in a loop. The problem isn't the LLM; it's the architecture. Simple, linear chains are dead. To build resilient, autonomous systems that can handle ambiguity and course-correct, you need to master the ReAct (Reason + Act) pattern . This guide breaks down the architectural shift from linear thinking to cyclical reasoning. We'll explore the core concepts and walk through a self-contained TypeScript implementation of a Customer Support Agent that can dynamically query a database. The Core Concept: From Linear Chains to Cyclical Reasoning In the early days of LLM applications, agents were largely linear. They would receive a query, reason about it once, execute a single tool, and produce a final answer. This works for simple tasks, but it breaks down when faced with complex, multi-step problems that req