Automatically Detect RAG Degradation After an LLM Update Using a LUMOS Multi-Agent Framework

Introduction Every new LLM release brings excitement—but for teams running retrieval-augmented generation (RAG) pipelines, it also brings risk. A model update can silently introduce embedding drift, citation errors, or latency increases that degrade downstream applications. Without automated safeguards, your production RAG system may serve inaccurate answers for days or weeks before someone notices. This article introduces a practical multi-agent framework built on LUMOS that continuously monitors RAG pipeline health, automatically triggers retuning when a new model is deployed, and logs every change for compliance. You'll learn how to deploy three specialized agents: a RAG Health Monitor , a Retuning Orchestrator , and a Governance Reporter . By the end, you'll have a blueprint to keep your enterprise RAG accurate and reliable across model release cycles. The Hidden Threat: How a New LL

M Release Can Break Your RAG Pipeline When a new LLM version ships, it often changes the embedding space, the tokenizer behavior, or the model's internal attention patterns. These changes can ripple through a RAG pipeline in three critical ways: Embedding drift : The vector representations of your documents shift relative to the model's new embedding function, reducing the similarity between queries and chunked knowledge base items. Citation inaccuracy : Retrieved passages may no longer match the answer the model generates, leading to hallucinated or unsupported claims. Increased latency : New model architectures or larger context windows can alter the retrieval and generation path, pushing response times beyond your SLO. For example, after upgrading from a Q2 2025 model to a Q4 2025 release, you might see retrieval precision drop from 92% to 78% overnight—without any changes to your doc

ument store or queries. This is exactly the scenario a multi-agent framework can catch and correct. Understanding Embedding Drift and Its Impact on Retrieval Accuracy Embedding drift is a measurable shift in the semantic coordinates assigned to text by an encoder. If your original pipeline used the encoder from model version A, and the new model uses encoder version B, the same query will produce a different vector. The cosine similarity between query and stored chunk embeddings can drop significantly. Measuring Embedding Drift 1. Take a holdout set of 1,000 production queries with known relevant documents. 2. Compute the original embeddings (from the previous model version) for both queries and documents. 3. Recompute embeddings using the new model version. 4. For each query, compare the cosine similarity to the known relevant chunk across old vs. new embeddings. A drop of more than 0.0

5 in mean similarity often indicates drift that will harm retrieval accuracy. For instance, if the average similarity falls from 0.82 to 0.74, expect recall to decrease by 5–10 percentage points. Impact on Citation Accuracy Citation accuracy measures how often every claim in the generated answer is actually supported by the retrieved documents. You can compute it as: After a model update, you might see this metric drop from 95% to 82% because the new model hallucinates facts not present in the retrieved chunks. The RAG Health Monitor agent tracks this in real time. Introducing LUMOS: A Multi-Agent Framework for RAG Pipeline Resilience LUMOS (Language Models Operating System) is an open-source platform designed to orchestrate intelligent AI agents at enterprise scale. It provides agent life-cycle management, tool integration, and an Agent Definition Language (ADL) for declaratively descri

bing agent behaviors. Its event-driven architecture makes it ideal for monitoring and maintaining services like RAG pipelines. In our framework, three agents run asynchronously within LUMOS: RAG Health Monitor : Continuously evaluates production queries and retrieval quality. Retuning Orchestrator : Triggers and manages pipeline variant A/B tests. Governance Reporter : Logs all changes for audit compliance. Each agent communicates via LUMOS's internal message bus and can invoke external tools (like a vector database client or a model inference API). Agent 1: The RAG Health Monitor – Detecting Degradation in Real Time The first agent acts as a continuous quality gate. It subscribes to a topic that fires whenever a new LLM version is deployed (or runs on a schedule). Its main tasks: Embedding drift check : Sample the current query embedding distribution and compare it to the baseline store

d from the last pipeline state. If drift exceeds a configurable threshold (e.g., mean cosine similarity drop 0.05), flag an alert. Citation accuracy monitor : Periodically review a random sample of recent answer–retrieved-document pairs. Use an automated judgment function (e.g., LLM-as-judge or exac