A Reusable, Layered Evaluation Framework for Multi-Agent Large Language Model Systems

Orchestrating and evaluating a multi-agent systems is now common problem in production. Their behavior is stochastic and multi-step and static, single-turn benchmarks measure which model is better, not whether a given pipeline has regressed and in which component. I present a reusable, layered evaluation framework for such systems which balance between cost and reliably. It orders its checks by cost—deterministic validators and computation metrics run first and for free, and a model-graded LLM-as-a-judge layer runs only where holistic judgement is needed. To test the framework, I applied it to a real document-processing agent built on two LangGraph pipelines: ingestion, which extracts text from PDFs, and chat, which retrieves passages and answers with citations. Gating the expensive judge layer behind the cheap ones cut judge spend by about 63% at no cost to coverage. A layered, stage-attributed framework therefore gives a live multi-agent pipeline a continuous, reproducible regression signal, localized to the component at fault. It also give real explainability and debuggability of such a complex system.