A Formal Decision Framework for Risk-Gated AI Execution Agents: Algorithms, Interfaces, and Control Logic for Governed Trading Bots

This paper specifies an implementation-ready decision framework for governed AI execution agents in digital-asset markets. The framework decomposes a trading agent into market-state estimation, signal calibration, eligibility gating, exposure proposal, risk-envelope projection, venue-aware execution, supervisor escalation, and decision-record audit.

It defines state variables, decision equations, a risk projection operator, an execution optimizer, a supervisor escalation policy, a model-promotion gate, and a minimum decision-record contract. Predictive models may propose exposure, but deterministic risk gates decide whether capital may be committed.