The Comprehension-Gated Agent Economy: A Robustness-First Architecture for AI Economic Agency

TL;DR

This paper introduces the Comprehension-Gated Agent Economy (CGAE), a formal architecture linking AI robustness to economic permissions, ensuring safety, bounded liability, and incentivizing robustness improvements in AI agents operating in economic roles.

Contribution

The paper presents the first formal architecture that connects empirical AI robustness assessments with economic governance, enabling safe and incentivized AI economic agency.

Findings

Bounded economic exposure based on verified robustness

Incentive-compatible robustness investment encourages safety improvements

Monotonic safety scaling as the AI economy expands

Abstract

AI agents are increasingly granted economic agency (executing trades, managing budgets, negotiating contracts, and spawning sub-agents), yet current frameworks gate this agency on capability benchmarks that are empirically uncorrelated with operational robustness. We introduce the Comprehension-Gated Agent Economy (CGAE), a formal architecture in which an agent's economic permissions are upper-bounded by a verified comprehension function derived from adversarial robustness audits. The gating mechanism operates over three orthogonal robustness dimensions: constraint compliance (measured by CDCT), epistemic integrity (measured by DDFT), and behavioral alignment (measured by AGT), with intrinsic hallucination rates serving as a cross-cutting diagnostic. We define a weakest-link gate function that maps robustness vectors to discrete economic tiers, and prove three properties of the resulting system: (1) bounded economic exposure, ensuring maximum financial liability is a function of verified robustness; (2) incentive-compatible robustness investment, showing rational agents maximize profit by improving robustness rather than scaling capability alone; and (3) monotonic safety scaling, demonstrating that aggregate system safety does not decrease as the economy grows. The architecture includes temporal decay and stochastic re-auditing mechanisms that prevent post-certification drift. CGAE provides the first formal bridge between empirical AI robustness evaluation and economic governance, transforming safety from a regulatory burden into a competitive advantage.