The Decay of Decision-Relevant Information Over Time

Context Rot is the progressive degradation of information relevance that occurs when context captured at one point in time is used for decisions at a later point. It represents the gap between when information was gathered and when it's applied—a gap during which the world may have changed in ways that make the original context misleading or obsolete.

The term "rot" is deliberately chosen to evoke biological decay. Just as organic matter degrades over time, losing its original properties, information degrades in its decision-relevance. A customer's credit profile from last month may not reflect this month's reality. A system's performance baseline from yesterday may not apply after today's deployment. A competitor analysis from last quarter may be obsolete after this quarter's market shifts.

Context Rot manifests in several patterns. Temporal rot occurs when time-sensitive information becomes stale: prices change, statuses update, relationships evolve. Structural rot occurs when the relationships between entities shift: organizational hierarchies change, system dependencies are modified, regulatory requirements update. Semantic rot occurs when the meaning of information changes even if the data doesn't.

For AI agents, Context Rot is particularly dangerous because agents may not recognize when their context is outdated. A human decision-maker often intuitively senses when information "feels old" or when circumstances have changed. An AI agent lacks this intuition—it will confidently apply stale context unless the system is designed to detect and flag potential rot.

Context OS addresses Context Rot through several mechanisms. First, context is timestamped with both capture time and expected validity period. Second, volatility indicators flag context that changes frequently. Third, decision-time validation verifies critical context immediately before consequential decisions. Fourth, anomaly detection identifies when context seems inconsistent with recent patterns.

The consequences of Context Rot compound in AI systems because of decision velocity. A human might make a few important decisions per day, providing opportunities to notice and correct for stale information. An AI agent might make thousands of decisions per hour. If those decisions are based on rotted context, the organization accumulates a large volume of potentially flawed decisions before anyone notices.

The solution to Context Rot isn't simply faster data refresh—it's context-aware decision-making. Agents must understand not just the content of their context but its temporal characteristics: when it was captured, how quickly it typically changes, and how sensitive the current decision is to potential staleness.

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