Infrastructure for Effective Human Oversight of Distributed AI Agents

Abstract

This paper proposes how institutions could preserve effective human oversight as AI agents increasingly help build, test and operate more capable AI systems. The architecture aims to connect independent observation, bounded delegation, challengeable safety conditions and intervention enforced at the resources where consequential actions occur. Agents may propose changes and request additional access; the authority to approve, restrict and restore that access remains outside their unilateral control. The paper follows a research task through delegation, a disputed safety-monitor change and human intervention. It explains how restrictions could reach running jobs, cached access and subordinate agents and how controllers would distinguish blocked actions, terminated workloads and unresolved outcomes. This V1.0 release includes the paper and a reproducible Python validation package. Across 12 constructed scenarios, six timing settings and two configurations, all 144 runs passed 348 checks. All 72 paired comparisons produced matching checks and effect traces. Three diagnostic safeguard removals exposed their intended failures, and repeated executions reproduced all four JSON outputs byte for byte. These preliminary results examine an abstract protocol. They do not establish deployed containment or an advantage over competent existing controls. The proposal is shared to invite technical challenge, infrastructure evaluation and collaborative refinement. The ZIP contains the Python runner, scenario traces and results, paired comparisons, diagnostic outputs, reproduction instructions and a SHA-256 integrity manifest. Python 3 with the standard library is sufficient. To reproduce the results, extract the ZIP, enter the Human_Oversight_Intervention_Validation_v1.0 directory and run: python3 run_intervention_simulation.py --output regenerated