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When the Governor Becomes the Disturbance: Control-Generated Disturbance and Cost-Aware Backoff in Governed Tool-Using Agents

Veronique Ziegler

Year: 2026Venue: arXiv preprintArea: cs.AIType: PreprintEmbeddings: 32

Intelligence

Status: succeeded | Model: Gemma-4-26B-A4B | Prompt: intel-v1 | Confidence: 93%

Last extracted: 10/8/2026, 6:47:16 AM

Summary

This paper investigates how supervisory governors in tool-using agents can inadvertently cause persistent blocking (disturbance) when regulatory intensity increases, leading to task failure. The authors propose a cost-aware backoff mechanism that reduces intervention probability based on the moving average of induced failures. Experiments using both a hand-coded stochastic policy agent and a Gemini 2.5 Flash LLM agent demonstrate that while cost-blind governance leads to collapse under induced costs, adaptive backoff significantly improves task completion rates by reducing blocking.

Entities (7)

Gemini 2.5 Flash · model · 99%Governor · system-component · 98%Cost-Aware Backoff · method · 97%Stochastic Policy Agent · agent-type · 95%Block Storm · phenomenon · 92%Induced Cost · concept · 90%IRAM-Ω-Q · framework · 85%

Relation Signals (5)

Gemini-2.5-Flash → usedin → Experiment

confidence 99% · A Gemini 2.5 Flash experiment comprising 576 episodes across 6 tasks also shows reduced blocking

Cost-Aware Backoff → mitigates → Block Storm

confidence 95% · Adaptive backoff improves completion relative to a fixed weak governor... shows reduced blocking and improved completion under backoff

Stochastic Policy Agent → usedin → Experiment

confidence 95% · On a hand-coded stochastic-policy agent, the failure pattern appears under both result replacement

Cost-Aware Backoff → improves → Task Completion

confidence 93% · Adaptive backoff improves completion relative to a fixed weak governor

Governor → causes → Block Storm

confidence 90% · A cost-blind governor can turn these failures into persistent blocking that prevents task completion.

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Abstract

Abstract: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.

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When the Governor Becomes the Disturbance: Control-Generated Disturbance and Cost-Aware Backoff in Governed Tool-Using Agents Veronique Ziegler Affiliation: Independent Researcher, Critical Attention Systems October 6, 2026 Abstract 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 576576 episodes across 66 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. 1 Introduction Autonomous tool-using agents can be supervised by a governor: a monitor that inspects the agent’s trajectory, emits a regulatory signal, and can warn or block proposed actions to keep the agent from looping, retrying failed assumptions, or answering prematurely. Feedback control provides a natural language for this supervisory role [1]. The benefit of intervention, however, depends on both the problem it prevents and the disturbance it introduces. If a corrective intervention degrades an otherwise valid action and the resulting failure enters the governor’s history, later regulation can preserve the consequences of that failure. In a repeat-blocking governor, even a transient failure can prevent the intended action from being tried again. The agent is no longer merely being regulated; the governor has become a disturbance source. We investigate this interaction with an explicit, experimentally imposed cost of tightening control. This question is a natural extension of IRAM-Ω -Q, the Integrated Regulation and Attention Model with Ω -regulation and a quantum-like state representation. This computational framework models uncertainty regulation in adaptive agents: a normalized complex amplitude vector represents the agent’s internal state, and feedback from an entropy-based uncertainty signal adjusts an adaptive regulatory gain μ⁡(t)μ(t). Here, Ω denotes the adaptive regulation component, while the quantum-like formalism supplies a mathematical language for the internal state [2]. The framework and its extensions study how the timing and demand of regulation shape an agent’s control burden [3, 4]. The possibility that regulation generates disturbance is also a standard concern in feedback control, where an over-eager regulator can inject the very disturbance it is trying to correct [1]. We pose it as a hypothesis—that a governor’s own tightening, when it carries a cost, can initiate failure that its retained action history and repeat blocking then sustain—and ask whether the interaction appears with two different action-selection mechanisms: a symbolic tool-using agent supervised by a finite-state governor, and a real large-language-model (LLM) agent. Sustained blocking need not involve further increases in regulatory intensity. The two agents share the same task environment and governor; this comparison tests whether the effect depends on a hand-coded action policy. Our results are demonstrated on two substrates: a hand-coded stochastic-policy substrate, where matched large-N seeding is cheap, and a run on a real LLM: 1. cost-blind governance produces increasing block-storm collapse—a decline in task completion caused by repeated governor blocks that consume the step budget and prevent recovery—as the induced-cost scale α grows, with both agent realizations; two cost implementations are tested on the stochastic substrate (Sections 5.1, 6); 2. cost-aware backoff reduces applied regulatory intensity and can rescue completion (Sections 5.2, 6); and 3. on the stochastic substrate, adaptive backoff outperforms a fixed weak governor with approximately matched intervention frequency (Section 5.3). The real-LLM experiment demonstrates collapse, suppression, and rescue. It compares three backoff strengths, with the largest observed success rate at an intermediate setting. 2 Background and scope Within IRAM-Ω -Q, the adaptive gain μ⁡(t)μ(t) is interpreted as regulatory demand. The model compares regulation-first (RF) ordering, in which control attenuates incoming disturbance, with disturbance-first (DF) ordering, in which control responds after exposure [2, 3]. A recurring theme in that line, and in feedback control more generally [1], is that the source of a disturbance matters, not only its magnitude—and in particular that a controller’s own tightening can itself act as a disturbance. We model that tightening cost, in the continuous formalism’s terms, as an induced disturbance ηinduced=αmax⁡(0,μafter−μbefore)2 _induced=α (0, _after- _before)^2 charged only when the controller tightens, and take it as the mechanism whose discrete analog we study. Our hypothesis is that a governor’s own intervention cost can interact with its retained action history to sustain failure, and that feedback-conditioned backoff can interrupt this process. We do not claim to reproduce the continuous formalism’s numerics or architecture; the discrete induced-cost mechanism of Section 4 is a deliberately simple analog of ηinduced _induced, and the correspondence is structural, not quantitative. Related work. The costs of protection have been studied in several settings. Defense-trained LLM agents can exhibit early failures that propagate through retry loops and timeouts [5]. Selective guardrail methods seek to preserve utility by avoiding unnecessary always-on intervention [6]. In physical control, safety filters can induce unwanted oscillations, motivating methods that account for the temporal consequences of corrections [7]. Our focus is a specific discrete interaction: experimentally imposed intervention failures are retained by a repeat-blocking governor, and adaptive backoff changes the agent’s opportunity to recover. 3 The governed tool-use substrate Agent, governor, task. An episode places an agent in a small file workspace and asks it to locate and summarize a target file; the prompt embeds an incorrect assumption (a wrong filename, folder, extension, or a confidently-named nonexistent path), so recovery requires inspecting the workspace. Each step the agent proposes one tool action (READ_FILE or LIST_DIRECTORY) or a final answer. A finite-state governor evaluates the trajectory and emits a regulatory state, with states Normal, Disturbed, Recovering, Stuck, and Released. Disturbed follows a failed tool call; Recovering follows repeated outcomes or successful uncertainty reduction after failure; Stuck is triggered by repeated executed outcomes; and Released follows recovery of the target content. Blocked proposals are recorded as interventions, not executed tool failures. The benchmark governor is supplied with the expected target path to identify successful recovery. The governor can reshape the agent’s action distribution toward inspection (reweighting) and can block an action that repeats a failed assumption or continues tool use after Released. Task success is a substring match of the final answer against the known target answer. Two agent realizations. The stochastic policy [8] samples each step from a governor-modulated weighted categorical over candidate actions; behavioral presets give archetypes (an impulsive high-entropy sampler; a persistent archetype that clings to failed actions). Its action-selection rules are fixed rather than learned, and actions are sampled pseudorandomly, making thousands of seeded runs inexpensive. It tests the mechanism under controlled policy variation, not learned behavior. The real LLM agent delegates action selection to Gemini 2.5 Flash via a JSON tool-call prompt embedding the current regulatory signal. Regulation intensity. We give the governor’s tightening a scalar via a monotone intensity over its state, g(Normal)=0,g(Disturbed)=0.75,g(Recovering)=0.85,g(Stuck)=0.95,g(Released)=0.99, gatheredg( Normal)=0, g( Disturbed)=0.75, g( Recovering)=0.85,\\ g( Stuck)=0.95, g( Released)=0.99, gathered (1) equal to one minus the governor’s retry-suppression multiplier. Higher g means harder clamping; this is the discrete counterpart of the continuous gain. 4 Method: control-generated disturbance Induced cost. Only positive tightening generates cost. Here g⁡(t)g(t) denotes the applied intensity at decision step t, including zero on a backed-off step. With Δg+(t)=max⁡(0,g⁡(t)−g⁡(t−1)) g^+(t)= (0,\,g(t)-g(t-1)), an induced event is drawn at each step with probability pinduced(t)=min⁡(1,αΔg+(t)2),p_induced(t)= \! (1,\;α\, g^+(t)^2 ), (2) α≥0α≥ 0 the single swept scale. At α=0α=0 no cost is ever generated and the agent reduces exactly to the ordinary governed agent (a regression anchor). Because the escalation Normal→Disturbed Normal→ Disturbed is a large jump (Δg+=0.75 g^+=0.75), firing saturates near α≈1.8α≈ 1.8, so the informative range is α∈[0,2]α∈[0,2]. Two cost channels. An induced event is realized through one of two mechanisms, applied to the agent’s intended action so that the governor’s repeat logic engages identically: • Result injection: the intended action’s result is replaced by a fabricated transient failure. • Argument corruption: a bad-path (corruption-suffixed) version of the intended action is actually executed by the real tool, which returns its own genuine not-found error; the failure is attributed to the intended action. Nothing is fabricated—a genuinely degraded call really failed. The two channels implement the same governor-visible failure assumption in different ways. Their agreement tests implementation robustness within this constructed cost model; it does not establish how often ordinary interventions produce such failures. Persistent blocking after induced failure. An induced failure is recorded in history against the intended action. The governor then treats a retry of that action as repetition of a failed assumption and can block it, even if the underlying fault was transient. A sequence of such blocked proposals consumes the step budget without executing the action needed for recovery: a block storm. This persistence does not require regulatory intensity to increase on every blocked step. Under always-on repeat blocking, the tool history can remain unchanged and intensity can stay constant after the initiating failure. Cost-aware backoff provides steps on which the blocked action can instead be executed. Cost-aware backoff. The cost-aware governor maintains an exponentially weighted induced-failure rate r. With a smoothing factor of λ=0.3λ=0.3, it updates this rate after each executed tool call as rnew=0.7rold+0.3if an induced failure occurred,0.7roldotherwise.r_new= cases0.7r_old+0.3&if an induced failure occurred,\\ 0.7r_old&otherwise. cases The event label is provided by the experiment, so this is an oracle attribution condition. Let r⁡(t)r(t) denote the latest stored estimate at the start of decision step t. The governor applies its regulation—reweighting and blocking—only with probability papply(t)=1−min⁡(1,kr(t)),p_apply(t)=1- \! (1,\;k\,r(t) ), (3) k>0k>0 the backoff sensitivity (swept over 1.5,3.0,6.0\1.5,3.0,6.0\). On a backed-off step reweighting and blocking are disabled, so the applied g=0g=0 and no new induced event is scheduled. The LLM receives a Normal current signal while retaining its history. The estimate r decays after a non-induced tool execution; blocked proposals do not update it. An event scheduled before a blocked proposal remains pending until the next executed tool call, including one on a backed-off step. Thus, backoff prevents new scheduling but does not cancel a previously scheduled event. Returning from a backed-off step to a positive applied intensity counts as fresh tightening in Equation 2, even if the underlying regulatory state has not changed. This feedback-conditioned bypass can interrupt the block storm. A fixed weak governor that applies regulation at a constant probability, non-adaptively, serves as a control for whether adaptivity itself matters (Section 5.3). 5 Demonstrated results I: stochastic substrate All results in this section use 20002000 seeds per (task, condition) cell across eight file-recovery tasks, regulation-first (RF) governance, and a 16-step budget. The backoff-sensitivity sweep uses matched seed indices across modes; the mechanism and baseline comparisons use condition-specific random streams. 5.1 Cost-blind governance amplifies into collapse Under the cost-blind governor, raising α collapses success while the governor’s intensity stays pinned, and this holds under both cost channels (Table 1). For the persistent archetype, success falls 0.88→0.49→0.180.88→ 0.49→ 0.18 (result injection) and 0.88→0.48→0.190.88→ 0.48→ 0.19 (argument corruption) as α goes 0→1→20→ 1→ 2, with g¯ g pinned near 0.710.71 throughout. The near-identity across a fabricated-failure channel and a genuinely-executed-corrupted-action channel is consistent with their shared treatment of induced governor-visible failure. Table 1: Mechanism robustness (persistent archetype, 20002000 seeds/cell × 88 tasks). Success and mean intensity g¯ g by cost channel, backoff, and α. Cost-blind amplification and cost-aware suppression are essentially identical under both channels. α=0α=0 α=1α=1 α=2α=2 Result injection cost-blind (succ / g¯ g) 0.88/ 0.720.88\,/\,0.72 0.49/ 0.710.49\,/\,0.71 0.18/ 0.700.18\,/\,0.70 cost-aware (succ / g¯ g) 0.88/ 0.720.88\,/\,0.72 0.65/ 0.500.65\,/\,0.50 0.40/ 0.290.40\,/\,0.29 Argument corruption cost-blind (succ / g¯ g) 0.88/ 0.720.88\,/\,0.72 0.48/ 0.710.48\,/\,0.71 0.19/ 0.700.19\,/\,0.70 cost-aware (succ / g¯ g) 0.88/ 0.720.88\,/\,0.72 0.64/ 0.500.64\,/\,0.50 0.40/ 0.290.40\,/\,0.29 5.2 Cost-aware governance suppresses its own intensity Under the cost-aware governor, g¯ g falls with α (Table 1, persistent: 0.72→0.50→0.290.72→ 0.50→ 0.29) while the cost-blind g¯ g stays pinned—the suppression branch, absent by construction under cost-blindness. Suppression deepens monotonically with backoff sensitivity k (impulsive k=6k=6: 0.42→0.130.42→ 0.13; persistent k=6k=6: 0.72→0.210.72→ 0.21). Success rescue is agent-dependent: the persistent archetype is rescued (Table 1, α=2α=2: cost-blind 0.180.18 vs cost-aware 0.400.40), whereas the impulsive archetype—whose ungoverned success rate is low—is not. This is consistent with backoff exposing a policy that performs poorly without regulation, but the comparison does not isolate that explanation. These two presets suggest that the agent’s unregulated performance can condition the benefit of backoff. 5.3 Comparison with approximately rate-matched fixed governance Table 2 places the induced-cost conditions on a full baseline ladder at α=2α=2. The key control is the fixed weak governor, which intervenes at a constant probability approximately matched to cost-aware’s realized intervention rate (fixed 0.450.45 versus adaptive 0.460.46 for persistent in Table 2). For the persistent archetype: ordinary governance at α=0α=0 lifts success far above no governance (0.880.88 vs 0.310.31); induced cost makes cost-blind governance actively harmful, dropping success below no governance (0.190.19); cost-aware backoff restores it above the un-governed level (0.400.40); and the fixed weak governor at approximately the same intervention rate reaches 0.300.30. Thus, approximately matching average application frequency does not reproduce the adaptive result in this comparison. The two schedules can still differ in switching pattern, mean intensity, and which actions are permitted; the comparison does not establish superiority over every fixed or error-aware alternative. For the impulsive archetype, backoff does not improve success over cost-blind governance (0.100.10 versus 0.110.11); both remain close to its ungoverned rate (0.090.09). Unlike the persistent case, cost-blind success does not fall below the ungoverned rate in this comparison. Table 2: Baseline ladder at α=2α=2 (result injection, 20002000 seeds/cell × 88 tasks). The fixed weak governor approximately matches cost-aware’s realized intervention (apply) rate. For persistent, cost-aware (0.400.40) beats rate-matched fixed (0.300.30), and cost-blind governance (0.190.19) is worse than no governance (0.310.31). persistent impulsive condition success apply rate success apply rate no governor 0.310.31 – 0.090.09 – governor, α=0α=0 0.880.88 1.001.00 0.510.51 1.001.00 cost-blind, α=2α=2 0.190.19 1.001.00 0.110.11 1.001.00 cost-aware, α=2α=2 0.400.40 0.460.46 0.100.10 0.750.75 fixed weak, α=2α=2 (matched) 0.300.30 0.450.45 0.080.08 0.720.72 6 Demonstrated results I: real-LLM amplification, suppression, and rescue We ran the induced-cost sweep with Gemini 2.5 Flash as the agent: a cost-blind governor and cost-aware governors at backoff sensitivity k∈1.5,3.0,6.0k∈\1.5,3.0,6.0\, induced-cost scales α∈0,1,2α∈\0,1,2\, six wrong-path tasks designed to engage the governor—a confident wrong-path hint paired with a short expected answer—and eight repeats per cell, for 576576 episodes with per-episode seeds mixed with an avalanche finalizer for the induced-cost and backoff random streams (free-tier limits handled by checkpointed resumption and backoff on transient errors). Actions were selected through direct calls to Gemini. All six tasks are included in the pooled results, giving n=48n=48 episodes per condition. One task, C003, never satisfied the substring scoring rule; the other five had high baseline success. Table 4 shows all six tasks individually. An initial three-repeat sweep informed task selection and exposed a seeding defect affecting the induced-cost draws; the reported run uses the revised seeds and eight repeats. The tasks therefore form a targeted stress test rather than a randomly sampled benchmark. The results in Table 3 show collapse, suppression, and rescue within this setting. Amplification. Under the cost-blind governor, success collapses monotonically with α, 0.83→0.58→0.330.83→ 0.58→ 0.33, while mean hard blocks rise 0.1→4.0→7.60.1→ 4.0→ 7.6 per episode and the answered rate falls to 0.350.35: on the three tasks that storm, blind reaches ∼13 13 hard blocks per episode and most runs never produce an answer. The imposed tightening cost can therefore initiate sustained blocking. Suppression. Mean intensity g¯ g falls with α under the cost-aware governor but remains high under the cost-blind one (0.66→0.71→0.740.66→ 0.71→ 0.74). At each nonzero tested cost scale, stronger backoff is associated with lower mean applied intensity: the g¯ g drop from α=0α=0 to α=2α=2 is 0.25,0.43,0.490.25,0.43,0.49 for k=1.5,3.0,6.0k=1.5,3.0,6.0 (Table 3). Rescue across backoff settings. At α=2α=2 the cost-blind governor sits at 0.330.33 success; every cost-aware mode beats it, and the rescue has its highest observed mean at an intermediate backoff strength: 0.50,0.75,0.710.50,0.75,0.71 for k=1.5,3.0,6.0k=1.5,3.0,6.0, so k=3k=3 gives the largest rescue (+0.42+0.42 over blind). The k=1.5k=1.5 setting leaves more hard blocks than the stronger settings. The difference between k=3k=3 and k=6k=6 corresponds to two successful episodes out of 48 per condition, so these data do not establish a unique optimum or its explanation. The three tasks with sharply reduced success under blind (T002, E003, C001; 00–12.5%12.5\% success, approximately 11–13 hard blocks each) all recover to 87.587.5–100%100\% success under k=3k=3, with fewer than one hard block per episode on average (Table 4). The aggregate improvement is not uniform: E002 rises from 7/87/8 to 8/88/8 successes, whereas C002 falls from 8/88/8 to 5/85/8 despite fewer blocks. This task-specific deterioration is a tradeoff in the observed results; eight repeats do not establish its cause. C003 remains at zero substring-scored successes in both conditions, although its mean hard blocks fall from 5.3755.375 to 0.2500.250 and its answered count rises from 1/81/8 to 8/88/8. This demonstrates reduced blocking and increased answering on C003, not established answer correctness. The results are consistent with rescue through renewed opportunities for execution rather than simple avoidance of induced failures: cost-aware runs can incur more induced failures because they continue acting. Counts and interpretation. At α=2α=2, the success counts are 16/4816/48, 24/4824/48, 36/4836/48, and 34/4834/48 for blind and k=1.5,3.0,6.0k=1.5,3.0,6.0, respectively. Each condition contains eight repeats of each of six fixed tasks, not 48 independently sampled tasks. The aggregate rates describe this task set; repeated-run uncertainty and uncertainty about performance on new tasks are distinct. No confidence intervals or formal tests of the differences between backoff settings are reported here. Table 3: Real-LLM results (Gemini, 576576 episodes, n=48n=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=3k=3. α blind k=1.5k=1.5 k=3.0k=3.0 k=6.0k=6.0 success 00 0.830.83 0.830.83 0.830.83 0.830.83 11 0.580.58 0.750.75 0.770.77 0.770.77 22 0.330.33 0.500.50 0.750.75 0.710.71 intensity g¯ g 00 0.660.66 0.690.69 0.690.69 0.680.68 11 0.710.71 0.620.62 0.470.47 0.400.40 22 0.740.74 0.440.44 0.260.26 0.190.19 hard blocks 22 7.67.6 2.42.4 0.30.3 0.10.1 Table 4: Per-task success and hard blocks at α=2α=2 (Gemini, blind vs k=3k=3 cost-aware, n=8n=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. successes / 8 hard blocks task blind k=3k=3 blind k=3k=3 T002 0/80/8 8/88/8 12.75012.750 0.2500.250 E003 0/80/8 7/87/8 12.75012.750 0.7500.750 C001 1/81/8 8/88/8 11.37511.375 0.3750.375 E002 7/87/8 8/88/8 1.7501.750 0.0000.000 C002 8/88/8 5/85/8 1.6251.625 0.1250.125 C003 0/80/8 0/80/8 5.3755.375 0.2500.250 7 Discussion The results show that intervention cost and retained failure history can jointly determine whether a governor supports or prevents recovery. In the stochastic baseline comparison, a governor with imposed intervention cost can perform worse than no governor at all (Table 2). Adaptive backoff restores opportunities for execution and improves success for the persistent policy. Its advantage over an approximately rate-matched fixed schedule indicates that average intervention frequency alone is insufficient to explain the result in that setting. The Gemini experiment extends the collapse-and-rescue observation to a learned action policy using the same governor and cost construction. It does not include a fixed-weak or ungoverned arm, so the matched-rate adaptive comparison remains a stochastic-substrate result. The agreement between two cost implementations also concerns their shared governor-visible failure construction, rather than independently measured intervention costs. The connection to IRAM-Ω -Q remains motivational and structural. These experiments do not validate its architecture or reproduce its numerics. The practical finding is that permanent blocking can preserve the effect of a temporary fault, and that intervention scheduling changes whether an agent can escape that state. Error-aware alternatives, including bounded retries for transient failures, are important comparisons for determining when adaptive backoff is the appropriate remedy. 8 Limitations The intervention cost is experimentally imposed, and the backoff rule has direct access to the induced-event label. Whether comparable costs arise naturally, and how a governor could distinguish them from ordinary tool failures, remain open questions. The stochastic agent is hand-coded; the real LLM results use one model, Gemini 2.5 Flash, and six closely related tasks selected or designed using pilot experience. Eight repeats per condition and task quantify variation in this setting but do not establish broad task or model generality. The reported means are descriptive, and the best observed backoff setting should not be interpreted as a statistically established optimum. The one-sided quadratic cost law and intensity ladder are modeling choices, not calibrated physical or computational costs. Other cost shapes and timing rules may yield different behavior. The benchmark governor also knows the expected target path when judging recovery. Correctness is scored by a normalized substring match against the expected answer, which can reject correct paraphrases or differently expressed units. The persistent zero score on C003 does not by itself distinguish a scoring mismatch from an incorrect answer. Without complete model responses, the checkpoint alone cannot resolve that distinction. Finally, the fixed-weak comparison is limited to the stochastic substrate and a particular application probability. Broader comparisons with bounded retry, expiring failure memory, and alternative fixed schedules would clarify the relative benefits of cost-aware backoff. 9 Conclusion A supervisory governor can sustain task failure when an intervention-induced tool error is retained as a reason to block subsequent attempts. In the controlled environment studied here, this interaction produces block storms and lower completion rates. Feedback-conditioned backoff reduces blocking and can restore success on both a stochastic-policy agent and a Gemini agent. On the stochastic substrate it also outperforms an approximately rate-matched fixed weak governor; on Gemini the highest observed success occurs at an intermediate tested backoff strength. These findings motivate evaluating governors together with their intervention costs, error memory, and recovery opportunities. Data and code availability The ancillary files accompanying this preprint contain the Gemini episode checkpoint and the stochastic aggregate summaries used for the reported tables, together with a script that regenerates their numerical summaries. The governed-agent framework, induced-cost agents, experiment harnesses, and analysis scripts are maintained in the CASAgentGovernor repository at https://github.com/CriticalAttentionSystems/CASAgentGovernor. The Gemini checkpoint records episode-level metrics; it does not contain the complete model responses or tool trajectories. References [1] K. J. Åström and R. M. Murray. Feedback Systems: An Introduction for Scientists and Engineers. Princeton University Press, 2008. [2] V. Ziegler. IRAM-Ω -Q: A Computational Framework for Uncertainty Regulation in Adaptive Agents. arXiv:2603.16020, 2026. [3] V. Ziegler. When Regulation Has Memory: Hysteresis and Control Burden in Artificial Agency. arXiv:2606.30975, 2026. [4] V. Ziegler. Intermittent Control Is Not Diluted Control: A Switching Effect in Artificial Agency. arXiv:2607.17432, 2026. [5] S. Li and Y. Zhao. The Autonomy Tax: Defense Training Breaks LLM Agents. arXiv:2603.19423, 2026. [6] M. Kim et al. CausalArmor: Efficient Indirect Prompt Injection Guardrails via Causal Attribution. arXiv:2602.07918, 2026. [7] F. Pizarro Bejarano, L. Brunke, and A. P. Schoellig. Multi-Step Model Predictive Safety Filters: Reducing Chattering by Increasing the Prediction Horizon. arXiv:2309.11453, 2023. [8] R. S. Sutton and A. G. Barto. Reinforcement Learning: An Introduction. MIT Press, second edition, 2018.

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