Data is often treated as a definitive input. A number arrives, a condition is met, and a contract executes. If the value is technically correct, the assumption is that the outcome must also be correct. In decentralized systems, this logic breaks down more often than it appears.
Accuracy does not exist in isolation.
Under normal conditions, market data behaves predictably. Prices move within expected ranges. Liquidity is distributed evenly. In those environments, accuracy is usually enough. Problems begin when conditions change and the same data no longer carries the same meaning.
A price can be correct and still misleading. A value can reflect the market while failing to reflect the situation. When volatility increases or liquidity fragments, data that looks accurate may describe a temporary distortion rather than an actionable signal.
This is why oracle failures often go unnoticed at first. Smart contracts execute exactly as designed. No errors are triggered. The system continues operating while assumptions quietly diverge from reality. By the time the mismatch becomes obvious, positions have already adjusted around it.
Context is what prevents this drift. Timing constraints, source weighting, and verification rules determine whether data should be acted on immediately, delayed, or ignored altogether. These decisions cannot be deferred downstream. They must be embedded into the data pipeline itself.
APRO treats data evaluation as a layered process rather than a single check. Information is examined not only for correctness, but for relevance under current conditions. This approach accepts a trade-off. Additional validation introduces latency. But latency is preferable to silent error.
In financial systems, acting quickly on incomplete context is often more dangerous than acting slightly later with clarity. Accurate data is only useful when the system understands when not to trust it.
Context is not extra information. It is the condition that makes information usable.
