Cognitive Leakage: A Unified Framework for AI Accountability in the Age of Autonomous Agents

As artificial intelligence systems transition from passive tools to autonomous agents capable of executingmulti-step tasks, a fundamental accountability gap has emerged: enterprises bear fiduciary responsibility forAI-related decisions, yet no technical standard exists for determining when this responsibility has been fulfilled. This paper introduces Cognitive Leakage — the acquisition, accumulation, or reinforcement of decision logic,behavioral patterns, or workflow abstractions by AI systems during inference interactions — as a unifyingframework for understanding AI accountability failures. We present a taxonomy of five distinct leakage types(Responsibility, Intent, Behavioral, Evidence, and Physical) and propose the Delta-1 Validity Condition, a formalframework requiring simultaneous satisfaction of three vertices — Observation Fragmentation, Learning SignalNeutralization, and Enterprise-side Trusted Recomposition — as the necessary and sufficient condition forResponsibility Completion. Unlike existing approaches that focus on risk reduction or compliance assertion, this framework provides abinary, non-delegable, and externally observable standard for AI accountability. This paper is intentionallynon-implementational and defines a formal condition for responsibility completion rather than proposing asystem architecture or deployment methodology.