Building an Agentic AI App Builder with LangChain and Multi-Agent LLMs
A technical overview of building an Agentic AI app builder with planner, architect, and coder agents using Python, LangChain, RAG, and structured handoffs.
Give every agent a narrow responsibility
Multi-agent systems become easier to validate when each agent owns a specific job. A planner clarifies the goal, an architect designs the system boundary, and a coder turns the plan into implementation steps.
Use structured handoffs
Free-form messages make agent workflows hard to debug. Typed outputs, acceptance criteria, and explicit assumptions make it easier to inspect and correct the work between steps.
Treat RAG as context control
Retrieval should provide relevant constraints, examples, and documentation. The goal is not more context; it is better evidence at the exact moment the agent needs it.