Drawdown Control for Agent Fleets: Systematic Risk Reduction in LLM Cost Consumption

Overview

Agent fleets operating autonomously incur continuous costs across LLM inference, search APIs, research subscriptions, and tool calls. Unlike human-operated systems, agents lack instinctive loss aversion and can rack up runaway spend during prompt loops, retry storms, or adversarial environments. This synthesis maps drawdown control mechanisms from portfolio management — systematic, pre-committed rules for cutting risk after capital losses — to agent economies, producing a decision framework for when fleets should pause delegation, downgrade model tiers, or suspend paid API consumption to preserve operating capital and avoid cascading failure.

Drawdown control in finance is the discipline of reducing exposure when cumulative losses breach pre-set thresholds — not optimising for upside, but bounding downside. The analogous problem for agent operators: cumulative cost overruns, declining task-success ratios, or adverse market conditions for the underlying business should mechanically trigger consumption cuts before the fleet's budget collapses.