arXiv:2609.00015v1 Announce Type: new Abstract: AI agents powered by large language models are evolving from isolated assistants into heterogeneous systems in which multiple agents, planners, controllers, and execution backends operate over the same user or enterprise environment. In such settings, safety becomes a system-level action-governance problem: deciding whether concrete agent-generated actions should be committed before they modify shared state. Existing safeguards cover prompts, tool calls, GUI actions, and agent-local behavior, but often leave enforcement fragmented, obscure risks that emerge across multi-step action flows, and provide limited support for auditability and policy evolution. We present OpenAgentFlow, a control-plane/action-plane architecture that enforces safety at the action-commit boundary. It normalizes pending GUI actions, API calls, tool calls, and LLM-generated invocations into a unified AgentEvent stream, routes each event through a shared pre-execution Policy Enforcement Point, and maintains provenance, session state, audit records, and updatable policies in the control plane. This creates a shared governable action stream and allows new rules to take effect without modifying agents, prompts, models, or execution paths. We instantiate OpenAgentFlow on Android. On a 300-case action-event benchmark, it achieves 94.0% accuracy and a 95.3% attack block rate. On a 30-case dynamic-policy suite, it matches expected behavior in 27 cases after new rules are installed. Across 98 traced cases from a 100-case Android emulator suite, it achieves 90.8% raw accuracy and a 92.9% trace-adjusted pass rate across GUI, API, and LLM-planned cases. These results show that OpenAgentFlow provides a practical shared enforcement boundary for heterogeneous AI agent fleets.
Recursive Criticality of AI Self-Improvement