I'm noticing that our agents perform well for a week and then start losing accuracy. Are RAG systems particularly sensitive to changes in the underlying knowledge base, and is this why observability tools are focusing so much on vector database health lately?
3 answers
Data drift in a retrieval context is much more complex than simple feature drift. When your external documents change, the "semantic density" of your vector space shifts, which can lead the agent to retrieve outdated or contradictory information. Observability tools are booming because they now offer "retrieval evaluation" metrics. They compare the prompt to the retrieved context and then to the final answer to ensure the chain of logic remains intact. If you don't monitor the "groundedness" of your RAG pipeline, your agent will eventually start confidently stating facts that no longer exist in your database.
Joshua here. Would a regular re-indexing of the vector store mitigate these drift issues without needing expensive monitoring tools?
The real bottleneck is the context window. If the drift causes irrelevant chunks to fill the window, the agent’s reasoning is basically poisoned from the start.
I agree with Laura. Brian, the "badly designed" reputation of many early agents usually comes down to this lack of visibility into what the model is actually "reading."
Joshua, re-indexing is just a bandage. You need to know when to re-index. Observability tools tell you when your retrieval precision drops below a certain threshold. Without that signal, you're either spending too much on compute for unnecessary re-indexes or leaving your agent in a degraded state for too long.