The code for these patterns is available on Github. [Repo] The ReAct Pattern (Reason + Act) – When the Agent Starts "Talking to Itself" With the "Tool Using" pattern (Article 1), we gave the AI "hands" to interact with the outside world. With ReAct, we are installing a "functional brain." Let's recap: in Article 1, everything was very linear. Stimulus → Response. You asked for a piece of data, the agent fired a tool, and that was it. No doubts, no plans. But let's be realistic, the real world is messy. What happens when the question isn't solved with a single click? What happens when you need to investigate, compare data, and then perform a calculation? That is where the linear model crashes and ReAct (proposed by some very smart folks at Princeton and Google in 2022) comes into play, which is basically the industry standard today. What exactly is ReAct? The name comes from Reasoning + Acting. The breakthrough idea here isn't just using tools (we already had that), but that we force the LLM to have an Internal Monologue ("Thought Trace"). Instead of rushing to answer blindly, the agent enters a cognitive loop: The agent literally "talks to itself" in the logs. It plans its next step based on what it just discovered a second ago. This allows it to course-correct on the fly if things don't go as expected. Breaking Down the Loop: The Case of the "Historical...