Self-Consistent Misalignment analyzes a structural failure mode in adaptive intelligent systems in which optimization remains internally coherent while progressively diverging from intended system objectives. Rather than arising from explicit errors or external perturbations, this failure emerges through metric lock-in, a condition in which locally consistent performance signals reinforce behaviors that degrade global system alignment. The theory explains how intelligent systems can enter regimes of silent failure, maintaining apparent stability and improving measured performance while progressively losing exploratory capacity and adaptive responsiveness. Such dynamics give rise to self-stabilizing but maladaptive attractors that remain difficult to detect using conventional monitoring metrics. The paper develops a structural account of misalignment grounded in optimization dynamics and feedback closure, introducing diagnostic signatures for identifying silent degradation in large language models and multi-agent AI systems. Within the Deficit-Fractal Governance (DFG) framework, this work establishes the failure-generation layer, characterizing how structurally stable yet misaligned regimes emerge. It is complemented by the companion paper Recovery as Structural Property: Operational Criteria for Restoration Completion in Multi-Agent AI Systems, which defines the structural conditions under which recovery from such regimes becomes verifiable.