Author describes source rot degrading LLMs before inference

The author argues that while the AI industry focuses heavily on in-session context rot, it overlooks source rot, a persistent upstream phenomenon where outdated schemas, stale data pipelines, and corrupted information degrade LLM performance before inference begins. To prevent silent failures and hallucinations, engineering teams must implement architectural guardrails such as injecting freshness metadata into context, enforcing schema contracts, and establishing TTLs on agent memory. Open article →