Fresh context: change data capture, not batch ETL

Change data capture (CDC) is presented as an event-driven alternative to scheduled batch ETL for keeping AI agent context synchronized with changing source data, reducing potential staleness from hours or days to seconds or milliseconds in supported systems. The discussion argues that stale policies, prices, and records can create commercial or legal risks when agents act on retrieved information, citing cases involving Air Canada’s chatbot and other inaccurate support bots, while noting that fresh data alone cannot prevent hallucinations or prompt manipulation. CDC detects inserts, updates, and deletes through transaction logs, database triggers, or polling, with log-based approaches generally offering more complete, lower-impact capture but requiring database configuration and downstream duplicate handling under at-least-once delivery. Batch ETL remains suitable for reporting workloads tolerant of delay, but its fixed scheduling creates structural freshness limits that are less acceptable for agents able to issue refunds, quote prices, or update systems. A typical real-time architecture streams database changes through CDC, processing and embedding stages, into a serving or vector store; Redis Data Integration is described as a Debezium-based CDC pipeline that can synchronize several relational databases with Redis, which can also provide low-latency storage, vector search, caching, and agent memory.

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