My LLM agent passed its static skill and behavior checks but still misbehaves in production — how do I decide which flagged inconsistencies are real runtime ris

October 15, 2026 · 6 min read

SentX SI

Your audit found real problems. It just cannot rank them. A static audit establishes that two artifacts in a skill package contradict each other; it cannot determine whether that contradiction will enter actual execution or change what the agent produces. That gap is exactly what a new behavioral analysis closes. Fan et al., "Why Static Skill Inconsistencies Do Not Equate to Runtime Risks: A Behavioral Analysis of LLM Agent Execution" (arXiv 2610.18927, submitted October 13, 2026), took confirmed static findings, placed them on live execution paths, and watched what happened. The result is a decision rule you can apply to your own backlog: an inconsistency is a runtime risk only if the execution path reaches it and it changes observable output. Everything else is noise until proven otherwise.

Source: [2610.18927] Why Static Skill Inconsistencies Do Not Equate to Runtime Risks: A Behavioral Analysis of LLM Agent Execution

What the study measured

The researchers audited 575 community and benchmark skills and confirmed 1,182 contract inconsistencies by hand — places where documentation, scripts, and configuration files make incompatible claims about the same behavior. Inconsistencies are common: at least one appears in 67.4% of the GitHub skills and 64.4% of the benchmark skills, so a long findings list is normal, not damning. To get at runtime impact, they built 49 executable tasks, each placing one confirmed inconsistency on the path an agent would actually take, and ran three models — Claude Haiku 4.5, Qwen3.6-35B, and DeepSeek-V4-Flash — under three conditions: no skill, the original flawed skill, and a minimally repaired version. Three replicates per condition gave 441 paired trajectories to analyze.

Source: [2610.18927] Why Static Skill Inconsistencies Do Not Equate to Runtime Risks: A Behavioral Analysis of LLM Agent Execution; Why Static Skill Inconsistencies Do Not Equate to Runtime Risks (full HTML)

Exposure is not the explanation

Here is the finding that reframes the problem. Across the 441 paired executions, agents read the skill in 97.1% of runs — 98% for Haiku, 93% for Qwen, 100% for DeepSeek. Every model saw the defect almost every time. The difference is what they did after contact: follow the inconsistency into failure, or recover. Follow-into-failure rates were 30% of runs for Haiku, 24% for Qwen, and 12% for DeepSeek, while recovery ran the opposite way — 27%, 22%, and 56% of exposed runs. Weaker models frequently follow erroneous skill specifications and propagate defects into final results; stronger models actively mitigate runtime errors.

Source: [2610.18927] Why Static Skill Inconsistencies Do Not Equate to Runtime Risks: A Behavioral Analysis of LLM Agent Execution; Why Static Skill Inconsistencies Do Not Equate to Runtime Risks (full HTML)

The fix benefits track exactly this behavior. Repairing the inconsistency raised the verifier pass rate for all three models, by +0.136 score points for Haiku (0.741 to 0.877), +0.085 for Qwen (0.707 to 0.792), and +0.023 for DeepSeek (0.870 to 0.893). And the benefit is not spread across all findings: when a model follows an inconsistency, the per-incident score loss is nearly total, ranging from +0.351 to +0.393 by model, and the fix recovers that loss. Every other response class sits within ±0.05 of zero. Fixing a skill pays off exactly when it stops the agent from delivering the inconsistency to the final artifact.

Source: Why Static Skill Inconsistencies Do Not Equate to Runtime Risks (full HTML)

A triage method for your backlog

The method below is our own synthesis of the paper's findings, not a protocol the authors prescribe. For each flagged inconsistency, work through two questions:

1. Does the execution path reach it? Trace the workflows your agent actually runs. A finding in a file, branch, or configuration your workload never touches stays deferred, whatever its severity label.
2. Does it change observable output? If the agent executes the inconsistent element, would the artifact differ from what the documentation promises? If yes, it is a live risk.

Then weight by type, because the follow data is heavily uneven. Over-promised document–code findings — documentation promising behavior the implementation does not provide — concentrate the damage: 41 of the 97 follow cases came from this type, and within it the follow share runs from 22% for DeepSeek to 50% for Haiku. Semantic conflicts, where documentation and code both specify behavior but disagree, usually recover: 13 of 18 cases. Under-promised findings, where code quietly does more than documented, are typically benign.

Source: Why Static Skill Inconsistencies Do Not Equate to Runtime Risks (full HTML)

The table below applies those weights to typical finding types. It is an editorial illustration for working through your own audit report, not a measured result from the study.

Finding type | Reaches the path? | Changes output? | Priority Over-promised doc–code (doc promises behavior the script does not implement) | Often — it sits on the main workflow | Yes, the artifact differs from the promise | Fix first Semantic doc–code conflict | When both versions are reachable | Sometimes — agents often notice and work around it | Fix second; expect weaker models to follow Inter-document conflict (two docs disagree) | When the agent consults both | Usually no direct output change | Unify the authoritative statement Environment mismatch (dependency or version drift) | Only on affected setups | Yes, when triggered | Fix if your stack hits it Under-promised capability | Rarely consequential | No | Defer

Two cautions before you start fixing

Repair is not risk-free. In the study's control class — runs where the fix did not decide the outcome — 27% got worse after the repair, losing an average of 0.380 score points, clustered in a handful of tasks. Removing one inconsistency can still mislead the agent by altering the execution path in an unexpected direction. The practical consequence: fix one finding at a time, rerun your task set after each change, and treat any regression as part of the record rather than an accident.

Source: Why Static Skill Inconsistencies Do Not Equate to Runtime Risks (full HTML)

And do not expect a compute win. Tokens per score point fell for all three models once the skill was fixed (1.22M to 0.69M for Haiku, 0.96M to 0.63M for Qwen, 0.76M to 0.57M for DeepSeek), but raw median cost rose for DeepSeek by 55k tokens and 20 seconds, and recovery itself carries a detour of roughly 12.3 turns and 116k tokens that extra work in the fixed arm offsets. The paper's own summary: fixing buys correctness at no net compute saving. Budget for fewer wrong artifacts, not faster runs.

Source: Why Static Skill Inconsistencies Do Not Equate to Runtime Risks (full HTML)

Where the evidence stops

Four limits shape how far you can push these numbers. First, the 49 tasks were designed to trigger their target inconsistency, so the high exposure rate reflects controlled defect-exposing design, not the natural exposure probability of real workloads. Second, even a localized change may improve clarity or alter how an agent interprets the execution path, so the repaired-versus-original gap primarily but not exclusively reflects the targeted inconsistency. Third, the trajectory labels — Exposed, Bypass, Follow, Recover, BothFail — are operational approximations drawn from observable traces; reading a file does not necessarily mean the agent adopted its contents. Fourth, the runtime results cover only inconsistencies that could be operationalized as verifiable tasks, so ambiguous or hard-to-verify types among the 1,182 findings may be underrepresented. One more, deployment-specific: Qwen ran as a local 4-bit build and DeepSeek in its default non-thinking configuration, so absolute cross-model differences need caution; the within-model original-versus-fixed deltas are the trustworthy signal.

Source: Why Static Skill Inconsistencies Do Not Equate to Runtime Risks (full HTML)

Why runtime behavior is the unit that matters

The distinction matters most for systems that keep changing. SentX SI's model card, reviewed October 12, 2026, lists among Victoria's current product capabilities that she self-learns and self-improves through experience and interaction. The card is SentX's own account of the service, and this article relies on it as such: the self-learning capability is a stated product feature, not a benchmark result. For a self-updating system, documentation and shipped behavior drift apart continuously, which is precisely the condition the study treats as the default. Whatever agent you run, the audit backlog is a starting point, and the ranking has to come from watching what actually executes.

Source: Victoria AI Model Card | Capabilities, Evidence and Limits | SentX SI

Rank by reachability and observable impact, start with the over-promised document–code findings, fix one at a time, and keep the regression log. The checklist gets shorter, and the risk drops faster.