Building a Reliable LangGraph Workflow: Plan-Execute-Validate (PEV), Automated Retries, and MCP Integration in One Template

Most LangGraph agent examples stop at "hello world." A basic planner, an executor that calls a search tool, and a printed final result. That is fine for a demo, but the moment you try to ship an autonomous agent in production, you immediately run into the same four problems: How do I prevent an agent from silently hallucinating missing details between steps? How do I build a self-correcting loop that catches bad tool outputs before they poison the final answer? How do I optimize costs so I'm not using expensive reasoning models for simple bookkeeping? How do I safely integrate this orchestration layer with enterprise data and capabilities? I spent time solving all of these in a real production environment—a regulated life sciences platform automating scientific workflows over tens of millions of research records—and packaged the result as a template anyone can fork: langgraph-plan-execute-validate . This post explains the decisions behind each piece. What is PEV and Why Does it Need a