Distributed Intelligence without Autonomous Authority: A Human-Centric Architecture for Scalable Reasoning

As artificial intelligence systems increase in capability, many contemporary architectures implicitly assume that greater intelligence should lead to greater autonomy. This coupling introduces systemic risk by allowing reasoning systems to influence or initiate irreversible actions without clear attribution of responsibility. This work proposes an alternative architectural approach in which intelligence is allowed to scale while autonomy and execution remain explicitly constrained. The architecture combines Distributed Hybrid Inference (DHI) with Architectural Limits of Autonomy (ALA), enforcing a strict separation between inference, decision, and action at the architectural level rather than through behavioral policies. Reasoning is performed in a distributed, heterogeneous environment that preserves disagreement, uncertainty, and multiple perspectives without converging into a single authoritative output. The system operates over distributed communication substrates, including mesh-like and future decentralized networks, while remaining independent of any specific topology or implementation. It is also compatible with heterogeneous and emerging compute substrates, including quantum accelerators, without granting additional authority or agency. The proposed architecture is non-agentic by design. It does not introduce artificial actors, goals, or autonomous decision-making. Instead, it provides a human-centric environment for collective reasoning, where all irreversible actions require explicit human intent and confirmation. By constraining consequences rather than cognition, this approach enables advanced artificial intelligence systems that remain accountable, understandable, and aligned with human responsibility, even as their reasoning capabilities surpass human-level performance.