As AI shifts from chatbots to autonomous agents, AWS argues data-layer consistency matters as much as speed — because an agent reads a stale replica as ground truth and acts on it (their example: a 2-second lag makes an inventory agent halt a sale despite 500 units in stock). Jordan and Riley get into "silent poison" and "hallucination debt" (agents writing wrong conclusions back as permanent memory), and the three patterns for matching a consistency model to each workload: Aurora Global Database / DSQL for high-stakes data, DynamoDB Global Tables with conditional writes for shared agent memory, and Keyspaces for high-velocity intake. Source: Consistency is the new latency: AI at the data layer — AWS Architecture Blog, Aug 18 2026 —  ...