The Multi-Agent Architecture for Education

A developer built a multi-agent architecture for automated curriculum production using LangGraph, splitting the pipeline into four specialized agents: Planner, Writer, QA, and Formatter. This approach improved quality and consistency compared to using a single large language model prompt, as specialization allows each agent to focus on a narrow task.

When we first started automating curriculum production , the obvious move was to throw a big prompt at Gemini and ask it to produce a full lesson. It worked - sometimes. The quality was inconsistent. A great lesson on Monday, a mediocre one on Tuesday, and nobody could explain why. The root issue: one LLM doing everything is like one developer handling design, backend, QA, and deployment simultaneously. You get output, but it's noisy. We split the pipeline into four specialized agents, each with a narrow job: 1. Planner Agent Takes the learning objective and breaks it into a structured outline - topics, subtopics, learning outcomes, estimated time per section. No content yet, just architecture. 2. Writer Agent Receives the outline section by section and writes the actual content. Has no visibility into other sections - this forces consistency through structure, not context. 3. QA Agent Reviews the full draft against the original learning objective. Flags gaps, redundancies, and places where the content drifted from the outcome. Returns a structured diff. 4. Formatter Agent Takes the approved content and outputs it in the exact format our LMS expects - SCORM metadata, section markers, media placeholders. LangGraph lets us define the flow as a state machine - each agent is a node, and we can branch conditionally. If QA flags major issues, the loop goes back to Writer. If it passes, it moves to Formatter. This is something you can't do cleanly with a linear LangChain pipeline. python from langgraph.graph import StateGraph workflow = StateGraph CurriculumState workflow.add node "planner", planner agent workflow.add node "writer", writer agent workflow.add node "qa", qa agent workflow.add node "formatter", formatter agent workflow.add conditional edges "qa", should revise, { "revise": "writer", "approve": "formatter" } After switching to this architecture: The key insight: specialization improves quality even for AI agents. A model doing one focused task outperforms the same model doing five tasks in a single prompt. Originally published at mostafafathy.com