Coherence-Based Alignment: A Structural Architecture for Preventing Goal Drift in Agentic AI Systems

Recent advances in agentic AI—including tool-using LLM agents, autonomous code-generation systems, and multi-agent orchestration frameworks—have shifted the safety problem from simple output alignment to the deeper challenge of goal stability and internal coherence. Agent-based systems can now plan, act, refine their own strategies, and even participate in training pipelines that create downstream agents. This introduces new risks: internal goal drift, deceptive alignment, self-inconsistent reasoning, and cross-generation divergence in systems that outwardly appear aligned. Existing alignment techniques—RLHF, constitutional AI, and deliberative alignment—primarily shape external behavior, but do not provide a mechanism to maintain internal structural order as capabilities scale. This paper proposes Coherence-Based Alignment (CBA), a structural architecture for stabilizing advanced agentic systems by measuring and regulating their internal coherence across three domains: (1) epistemic coherence between world-model beliefs and observations, (2) action coherence between final actions and the agent’s own best-reasoned policy, and (3) value coherence between long-term trajectories and the agent’s declared or learned objectives. We formalize a scalar coherence metric C∈[0,1]and demonstrate how it can be incorporated into training (as a regularizer), inference (as a self-evaluation checkpoint), and multi-agent evolution (as a drift-detection mechanism). We further show how this architecture generalizes to scenarios where agents help train successor agents, offering the first principled method of detecting and constraining cross-generation misalignment. In addition to the theoretical framework, we present a preliminary empirical evaluation demonstrating that coherence constraints suppress reward-hacking behavior and improve long-horizon stability in a Gridworld environment. CBA provides a complementary, architecture-level approach to alignment that strengthens existing behavior-focused techniques. As agentic AI becomes more autonomous and self-directed, maintaining internal coherence may be necessary—not optional—for preventing emergent scheming and ensuring long-term goal stability.