Controlled Agentic AI Systems: A Governance-Driven Architecture for Auditable and Reproducible Decision Pipelines

Artificial intelligence systems deployed in safety-critical and regulated environments require not only predictive performance, but also strict adherence to operational constraints, auditability, and reproducibility. However, in most contemporary architectures, governance is treated as an external or post hoc mechanism, limiting the ability to ensure consistent and verifiable decision execution. This paper introduces Controlled Agentic AI Systems (CAIS), a formal architectural framework in which governance is embedded directly into the decision pipeline as a deterministic operator. The proposed formulation integrates a decision model, a constraint specification, and a governance operator that transforms proposed actions into admissible executed actions. The framework further defines audit trace semantics and replayability conditions, enabling deterministic reconstruction of decision trajectories. Theoretical analysis demonstrates that, under standard regularity assumptions, governance can be modeled as a non-expansive projection that enforces constraint-aware decision transformation while inducing bounded decision drift. This provides formal guarantees that governance does not destabilize system dynamics under perturbations. To evaluate these properties, we implement a reference CAIS architecture and conduct controlled experiments in multi-agent and federated simulation environments. The results show that embedding governance significantly reduces the frequency and severity of constraint violations across a range of scenarios. Projection-based repair consistently outperforms approval-only strategies, achieving near-complete compliance in structured regimes while maintaining bounded intervention costs. Importantly, governance does not degrade stability or convergence in federated settings and, in some cases, reduces action-level variance induced by distributed training. While strict feasibility cannot be guaranteed in all practical settings due to approximation and solver limitations, the empirical findings confirm that governance acts as a stabilizing transformation that consistently improves compliance without introducing destabilizing effects. The CAIS framework establishes governance as a first-class architectural component of agentic AI systems, providing a unified foundation for designing constraint-aware, auditable, and reproducible decision pipelines in regulated environments.