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7/12/2026
Day 1/30: ReAct Pattern Explained

Day 1/30: ReAct Pattern Explained

Short summary

A day-1 tutorial in a 30-day series explaining the ReAct (Reasoning, Action, Context) pattern for building agentic AI systems, using a customer support bot as the example. The author implemented a state graph with LangGraph and MCP to handle product and pricing inquiries with conversation context. Includes Python code showing state nodes, conditional edges, and reasoning/action/context functions.

  • •ReAct pattern has three components: Reasoning, Action, and Context for maintaining agent state
  • •Tutorial uses LangGraph and MCP to build a support bot handling product and pricing inquiries
  • •Includes Python code with state graph nodes, conditional edges, and reasoning functions

Generated with AI, which can make mistakes.

#ai-agents#ai-tools#certification-education
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