Current AI agent reliability efforts primarily focus on model tuning and retrieval mechanisms like prompts and rankers, often overlooking whether context remains relevant once it reaches the model. This gap highlights a critical issue where agents may act on outdated or unverified context. The author addresses this by developing approaches to ensure context validity before processing.