AI Context Failures Reveal Nine Patterns

At Vistage Chairworld 2026 in San Diego last week, a practitioner outlines nine common AI "context failures" observed in conversational agents. He argues most failures occur after retrieval—models often misapply, degrade, or fabricate contextual signals—naming issues such as context rot, poisoning, confusion, dilution, leakage, hallucination, and staleness. The piece urges practitioners to diagnose specific post-retrieval errors rather than over-tuning data pipelines.