You will understand what multi-agent systems are, why splitting work across specialized agents tends to produce sharper results than one giant prompt, and how EngineerPrep's own lesson-generation pipeline is built exactly this way.
Picture a single employee who is asked to simultaneously plan a project, write all the deliverables, and audit every line for errors — all at once, in one sitting, without pausing. That employee will do an okay job at each part. But they will struggle to do a great job at all three, because great planning, great writing, and great auditing require completely different mental modes. Now picture an AI. A single LLM prompt has the same problem. You ask it to plan, write, and review all at once, and it must satisfy all three goals within one response. The result is often mediocre across the board. EngineerPrep hit this wall early. The team wanted to auto-generate structured, high-quality lessons. One big prompt gave back something that looked like a lesson but read like a first draft that nobody reviewed. The question became: what if each job — planning, writing, reviewing…
An agent (in AI terms) is simply a program that takes some input, calls an AI model to decide what to do, and then acts on that decision. Think of it as a worker with a specific job title. A multi-agent system is just several of those workers arranged so they hand work to each other — like an assembly line in a factory. Here is the everyday analogy. Imagine a small restaurant kitchen. - The head chef reads the order and decides what dishes to make (the planner agent). - The line cook actually prepares each dish (the author agent). - The expediter at the pass checks every plate before it leaves (the reviewer agent). No single person does all three jobs at once. Each person is focused, skilled in their role, and hands the result to the next person only when their part is done. That is a multi-agent system.…
Imagine a timeline moving left to right. A REST request arrives: 'Generate a lesson on Java concurrency.' Step 1 — The Orchestrator receives the request. Picture a traffic controller standing at a crossroads. It does not do any AI work itself. It simply holds the request and knows which agent to call first. Nothing has been generated yet. Step 2 — The Planner Agent runs. The orchestrator hands the topic to the LessonPlannerAgent . This agent sends a focused prompt to Claude on Amazon Bedrock: 'Given this topic, produce a structured list of chapters with types and teaching objectives.' Claude returns a JSON plan — a skeleton with no prose yet. The orchestrator receives that plan and stores it. Step 3 — The Author Agent runs, once per chapter. The orchestrator loops over each chapter in the plan and calls the LessonAuthorAgent with: 'Here is the full plan for context.…