Such a bolt-on architecture wasn’t ideal but neither was it the deal-killer that AI makes it. Every arrow in the flow is a network hop, and every hop adds serialization overhead. Making matters worse, every separate system introduces a new consistency model. You are paying a tax in complexity, latency, and inconsistency when you split what should be one logical context into six different physical systems. AI is uniquely sensitive to this tax.
When a normal web app shows stale data, a user might see an old inventory count for a few seconds. When an AI agent retrieves inconsistent context (perhaps fetching a vector that points to a document that has already been updated in the relational store), it constructs a plausible narrative based on false premises. We call these hallucinations, but often the model is not making things up. It is being fed stale data by a fragmented database architecture. If your search index is “eventually consistent” with your system of record, your AI is “eventually hallucinating.”
How about if your transactional system is the source of truth, but your vector index updates asynchronously? Well, you’ve built a time lag into your agent’s memory. If your relationship data is synced through a pipeline that can drift, your agent can “know” relationships that are no longer true. If permissions are checked in one system while content is fetched from another, you are one bug away from data leakage.
