Supervisory governors can interfere with the tool-using agents they regulate. We study this possibility in a controlled file-recovery environment where increases in regulatory intensity trigger experimentally imposed tool failures. A cost-blind governor can turn these failures into persistent blocking that prevents task completion. We compare this governor with a backoff rule that reduces intervention probability using a moving average of known induced events. On a hand-coded stochastic-policy agent, the failure pattern appears under both result replacement and execution of corrupted tool arguments. For the persistent policy, adaptive backoff improves completion relative to a fixed weak governor with approximately matched intervention frequency. A Gemini 2.5 Flash experiment comprising 576 episodes across 6 tasks also shows reduced blocking and improved completion under backoff; among the tested settings, intermediate backoff strength achieves the highest observed aggregate success. These results identify an interaction between intervention cost and persistent action blocking, together with a possible mitigation. The cost mechanisms are imposed and their induced events are directly observable to the backoff rule; applicability beyond this controlled environment remains an empirical question.
Figures & tables
α=0
α=1
α=2
Result injection
cost-blind (succ / gˉ )
0.88/0.72
0.49/0.71
0.18/0.70
cost-aware (succ / gˉ )
0.88/0.72
0.65/0.50
0.40/0.29
Argument corruption
cost-blind (succ / gˉ )
0.88/0.72
0.48/0.71
0.19/0.70
cost-aware (succ / gˉ )
0.88/0.72
0.64/0.50
0.40/0.29
Table 1: Mechanism robustness (persistent archetype, 2000 seeds/cell ×8 tasks). Success and mean intensity gˉ by cost channel, backoff, and α . Cost-blind amplification and cost-aware suppression are essentially identical under both channels.
persistent
impulsive
condition
success
apply rate
success
apply rate
no governor
0.31
–
0.09
–
governor, α=0
0.88
1.00
0.51
1.00
cost-blind, α=2
0.19
1.00
0.11
1.00
cost-aware, α=2
0.40
0.46
0.10
0.75
fixed weak, α=2 (matched)
0.30
0.45
0.08
0.72
Table 2: Baseline ladder at α=2 (result injection, 2000 seeds/cell ×8 tasks). The fixed weak governor approximately matches cost-aware’s realized intervention (apply) rate. For persistent, cost-aware ( 0.40 ) beats rate-matched fixed ( 0.30 ), and cost-blind governance ( 0.19 ) is worse than no governance ( 0.31 ).
α
blind
k=1.5
k=3.0
k=6.0
success
0
0.83
0.83
0.83
0.83
1
0.58
0.75
0.77
0.77
2
0.33
0.50
0.75
0.71
intensity gˉ
0
0.66
0.69
0.69
0.68
1
0.71
0.62
0.47
0.40
2
0.74
0.44
0.26
0.19
Table 3: Real-LLM results (Gemini, 576 episodes, n=48 per cell, pooled over all six tasks). Cost-blind amplifies (success falls, blocks storm, intensity remains high); cost-aware suppresses its intensity in a monotone k dose-response and rescues success, with the highest observed success at k=3 .
successes / 8
hard blocks
task
blind
k=3
blind
k=3
T002
0/8
8/8
12.750
0.250
E003
0/8
7/8
12.750
0.750
C001
1/8
8/8
11.375
0.375
E002
7/8
8/8
1.750
0.000
C002
8/8
5/8
1.625
0.125
Table 4: Per-task success and hard blocks at α=2 (Gemini, blind vs k=3 cost-aware, n=8 per cell). All six tasks are shown; success is reported as a count out of eight to avoid rounding ambiguity. T002, E003, and C001 recover, whereas C002 has fewer successes under backoff. Block means are shown to three decimal places, including C003.