Bias, Noise, and Blind Spots: An Anatomy of Agentic Failures

This conceptual preprint proposes a two-coordinate anatomy of failure in AI agents. It distinguishes four modes: bias as systematic directional distortion, noise as unjustified variability, blind spots as deficits in material observability and reconstructability, and drift as change across time, versions, environments, or accumulated state. It then locates these modes across framing, access and retrieval, selection, representation, inference, presentation, action, memory, coordination, and oversight. The paper introduces a Failure Anatomy Matrix, explains how initially random variation can become durable bias through action, memory, and reuse, and proposes a minimum audit bundle based on repeated trials, controlled counterfactuals, material provenance, temporal comparison, and external-state verification. The framework is conceptual and is not presented as an empirically validated or exhaustive taxonomy.