🤖 AI Summary
This work addresses a critical limitation in conventional execution-signal-based governance mechanisms, which can only assess actions locally and thus fail to detect runtime deviations from the global behavior space \( \mathcal{A}_0 \) established during admission. To overcome this, we propose the Invariant Monitoring Layer (IML), which leverages direct access to the generative model of \( \mathcal{A}_0 \) to achieve bounded-delay detection of behavioral drift within structural blind spots of existing enforcement mechanisms. We first establish an information-theoretic non-identifiability theorem, proving the fundamental impossibility of such monitoring under prior approaches, then introduce the IML architecture—integrating formal verification, σ-algebraic analysis, and trajectory-level behavioral modeling—to circumvent this barrier and provide provable upper bounds on detection latency. Empirical evaluation across four scenarios, including n8n webhook pipelines and LangGraph StateGraph agents, demonstrates that while traditional mechanisms issued no alerts, IML successfully detected all drift types within 9–258 steps.
📝 Abstract
Autonomous agent systems are governed by enforcement mechanisms that flag hard constraint violations at runtime. The Agent Control Protocol identifies a structural limit of such systems: a correctly-functioning enforcement engine can enter a regime in which behavioral drift is invisible to it, because the enforcement signal operates below the layer where deviation is measurable. We show that enforcement-based governance is structurally unable to determine whether an agent's behavior remains within the admissible behavior space A0 established at admission time. Our central result, the Non-Identifiability Theorem, proves that A0 is not in the sigma-algebra generated by the enforcement signal g under the Local Observability Assumption, which every practical enforcement system satisfies. The impossibility arises from a fundamental mismatch: g evaluates actions locally against a point-wise rule set, while A0 encodes global, trajectory-level behavioral properties set at admission time. We define the Invariant Measurement Layer (IML), which bypasses this limitation by retaining direct access to the generative model of A0. We prove an information-theoretic impossibility for enforcement-based monitoring; separately, we show IML detects admission-time drift with provably finite detection delay, operating in the region where enforcement is structurally blind. Validated across four settings: three drift scenarios (300 and 1000 steps), a live n8n webhook pipeline, and a LangGraph StateGraph agent -- enforcement triggers zero violations while IML detects each drift type within 9-258 steps. Paper 2 of a 4-paper Agent Governance Series: atomic boundaries (P0, 10.5281/zenodo.19642166), ACP enforcement (P1, arXiv:2603.18829), fair allocation (P3, 10.5281/zenodo.19643928), irreducibility (P4, 10.5281/zenodo.19643950).