Building an AI feature is easy. Building a reliable multi-agent pipeline that coordinates four specialized AI agents, persists intermediate state, skips already-completed work on retries, and keeps the API responsive while the models think — that is the hard part.
This post walks through the architecture behind Clause AI, a platform that analyzes rental and lease agreements. It extracts key terms, flags risky clauses, and lets users chat with their contracts using RAG (Retrieval-Augmented Generation) — all powered by a coordinated pipeline of specialized AI agents.
The Problem with "Just Call an LLM"
The naive approach to building an AI-powered document analysis tool would be a single function that calls an LLM, parses the output, and saves it to a database. It works until it doesn't.
The moment you introduce multiple steps — parsing, summarizing, embedding, risk analysis — things break:
