How stale AI infrastructure snapshots silently produce confident wrong answers

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When an AI assistant like Claude Code reads a cached snapshot of cloud infrastructure, it can deliver authoritative-sounding but outdated answers — for example, reporting a dead-letter queue as missing hours after it was added. Infrawise, a tool that maps AWS and database infrastructure into a graph served to AI editors via MCP, faces this challenge directly since re-querying live AWS APIs on every assistant call is too slow and resource-intensive. To manage this, Infrawise caches infrastructure data in local JSON files with a strict TTL check that returns null — not stale data with a warning — when an entry expires, forcing a fresh analysis. All infrastructure data, including the graph, findings, and raw metadata, shares a unified 24-hour TTL after an earlier mismatch between a 1-hour metadata cache and a 24-hour graph cache caused subtle rebuild bugs. The core design principle is that bounded, explicitly invalidated staleness is safer than silently drifting context that feels current but is not.

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