The Magnum Opus: The Detektor-Safe Harness - A Computational Protocol for Non-Collusive Autonomous Orchestration and ASI Alignment in Perishable Inventory Markets

The distribution architecture of perishable-inventory travel markets airline network revenue management and hospitality asset optimization is undergoing a structural transition from human-mediated heuristics and static rule engines toward fully autonomous, machine-to-machine (M2M) agentic orchestration. In this emerging regime, non-deterministic pricing agents built on multi-agent reinforcement learning (MARL) and deep Q-network (DQN) architectures transact, negotiate, and reprice against one another across global distribution systems (GDS), channel managers, and property management system (PMS) interfaces at velocities that exceed both human oversight capacity and the latency assumptions embedded in existing revenue management control theory. This paper identifies and formalizes the Agent-to-Agent Yield Paradox: the phenomenon whereby individually rational, high-performing inferential agents produce collectively degraded outcomes when coupled through high-velocity distribution loops. We model two coupled failure channels. The first is compounding reliability decay, in which per-step stochastic error in agent inference tolerable in isolation multiplies across chained M2M interactions; under plausible chain depths and per-hop error assumptions, we derive stylized aggregate reliability degradation on the order of an order of magnitude or more (a bound we parameterize rather than assert empirically, and subject to sensitivity analysis in Section 6). The second is emergent tacit coordination: the now well-documented capacity of independent learning agents to converge on supracompetitive pricing equilibria without communication, agreement, or intent a behavior that sits directly in the crosshairs of active enforcement postures at the US Department of Justice, the UK Competition and Markets Authority, and recent state-level statutory initiatives targeting algorithmic pricing. As an engineering response, we introduce the Detektor-Safe Harness, a protocol instantiating a strict architectural bifurcation between an Inferential Layer (probabilistic, GPU-resident agents that generate candidate yield strategies) and a Computational Layer (a rigid, CPU-executed, deterministic outer harness that intercepts, validates, bounds, and where necessary overrides candidate rates before they reach distribution networks). The harness operationalizes three formal control primitives: (i) a global constrained optimization objective that maximizes net operating income subject to an explicit penalty on agentic volatility ; (ii) a Velocity Circuit Breaker, a deterministic conditional state machine transitioning from PASS to OVERRIDE when realized rate-change velocity breaches a pre-registered threshold ; and (iii) a Non-Collusion Boundary, an input-isolation condition that anchors terminal pricing authority to internal, sovereign asset telemetry occupancy, cost basis, cancellation probability curves, booking velocity rather than to competitor rate signals, thereby constructing an auditable architectural defense against tacit-coordination liability. The protocol is grounded in the Yield Equilibrium Protocol (YEP) family of governance frameworks and is validated through a comparative simulation design contrasting an unconstrained MARL ecosystem against a harness-governed environment, instrumented with guest cancellation logs, booking velocity telemetry, and empirically calibrated cancellation probability curves. We argue that the harness does not constitute price coordination in any legally cognizable sense; it constitutes structural market stabilization the algorithmic analogue of exchange circuit breakers and we develop this defensive position in dialogue with the current enforcement literature. Contributions are fourfold: a formal statement of the Agent-to-Agent Yield Paradox; a complete architectural and mathematical specification of a deterministic harness for perishable-inventory markets; and a compliance-by-architecture doctrine that converts antitrust exposure from a behavioral question (what did the algorithm learn?) into a verifiable structural question (what inputs was the algorithm permitted to act upon?). The fourth contribution extrapolates the protocol to hyper-rational and superintelligent regimes (Section 6.5.1), establishing the harness as a capability-invariant alignment safeguard. Beyond its contemporary engineering function, we advance a structural thesis with direct consequences for macro-scale AI alignment. Because every guarantee the harness provides bounded velocity, asset anchoring, the Non-Collusion Boundary is enforced on the action space of the governed policy rather than on its cognition, those guarantees are invariant to the intelligence of that policy. Section 6.5.1 formalizes the limiting case: as per-hop inferential error and policy rationality approaches perfect game-theoretic foresight, the harness’s reliability function vanishes by construction while its non-coordination function strictly strengthens, transitioning the protocol from an operational volatility circuit breaker into an infrastructure-level rollcage for artificial superintelligence (ASI) operating on the commercial grid. Compliance-by-architecture, on this reading, is not merely an antitrust posture; it is a deployable, statically verifiable primitive for aligning the actions of arbitrarily capable pricing intelligences precisely where their cognition cannot be audited. Keywords: Agentic AI governance; multi-agent reinforcement learning; algorithmic collusion; network revenue management; harness engineering; perishable inventory; deterministic control layers; machine-to-machine commerce; artificial superintelligence (ASI) alignment; compliance by architecture; capability-invariant control