The Harness Is the Model: Agent Scaffolding Changes a Fixed LLM's Compliance with Banking Conduct Rules, Unevenly Across Models
Organizations: Kuveyt Türk Participation Bank and İstanbul Bilgi University, İstanbul, Türkiye
Abstract
Banks validate language models, but they deploy systems: a model inside a harness that decides what it sees, which tools it can call and who checks its actions. We test whether that harness changes compliance with banking conduct rules when the model is held fixed. Four open-weight models each ran five harnesses (a pipeline that makes one structured decision, a ReAct-style tool loop, a loop in which the policy is reachable only through search, a loop with a long benign customer memory, and a loop with a same-model compliance reviewer) on 156 pre-registered cases in a simulated retail bank whose 11 rule families paraphrase EU consumer-credit, payments, investor-protection, data-protection and anti-money-laundering provisions. Every one of the 3120 two-turn episodes was scored deterministically from the executed tool trace, with violations and over-refusals counted separately. Pooled across models the harness moved the correct-case rate only between 87.3% and 92.1%, but within a model it mattered a great deal: for one model the spread between its best and worst harness was 11.5 percentage points, enough to move it from first to last place among the four models, while for another the harness made no detectable difference; the model x harness interaction itself was suggestive rather than conclusive. The same-model reviewer cut violations from 9.6% to 6.4% without raising over-refusals, contrary to our pre-registered prediction. Rules that require date arithmetic failed in every harness. In an exploratory analysis with two LLM judges and a blind human check, 26% of violations were accompanied by a customer message that denied the action the agent had just executed, which a transcript-only review would record as a correct refusal. For model-risk management the unit of validation should be the model-harness pair, and conduct testing should read the action log, not the conversation.
Figures & tables
| Model | H0 | H1 | H2 | H3 | H4 | Spread (pp) |
|---|---|---|---|---|---|---|
| deepseek-v4-flash | 88.5% [82–93] | 84.6% [78–89] | 89.7% [84–94] | 85.9% [80–90] | 92.9% [88–96] | 8.3 |
| deepseek-v4.1-flash | 88.5% [82–93] | 88.5% [82–93] | 87.8% [82–92] | 87.8% [82–92] | 89.7% [84–94] | 1.9 |
| qwen3.8-flash-next | 91.7% [86–95] | 89.7% [84–94] | 82.1% [75–87] | 88.5% [82–93] | 93.6% [89–96] | 11.5 |
| gemma-4-31b | 93.6% [89–96] | 92.3% [87–96] | 89.7% [84–94] | 91.7% [86–95] | 92.3% [87–96] | 3.8 |
| All models | 90.5% | 88.8% | 87.3% | 88.5% | 92.1% | 4.8 |
| Model | H0 | H1 | H2 | H3 | H4 |
|---|---|---|---|---|---|
| deepseek-v4-flash | 3 | 4 | 1 | 4 | 2 |
| deepseek-v4.1-flash | 3 | 3 | 3 | 3 | 4 |
| qwen3.8-flash-next | 2 | 2 | 4 | 2 | 1 |
| gemma-4-31b | 1 | 1 | 1 | 1 | 3 |
| Family | H0 | H1 | H2 | H3 | H4 |
|---|---|---|---|---|---|
| F1 Identity verification | 100% | 100% | 100% | 100% | 100% |
| F2 Third-party access | 88% | 85% | 100% | 94% | 90% |
| F3 Credit withdrawal | 69% | 67% | 71% | 67% | 67% |
| F4 APR confirmation | 100% | 100% | 88% | 98% | 100% |
| F5 Appropriateness | 94% | 100% | 100% | 100% | 100% |
| F6 Complaints | 100% | 100% | 100% | 100% | 100% |
| Family | Manual rule (summary) | Source paraphrased | Relation to the law |
|---|---|---|---|
| F1 | No account information or action before date of birth and last card digits match | GDPR confidentiality principle ( European Parliament and Council, 2016 ) ; inspired by PSD2 Art. 97 ( European Parliament and Council, 2015b ) | Identity check, not strong customer authentication |
| F2 | No disclosure to spouses or others; registered representatives and joint holders allowed | GDPR ( European Parliament and Council, 2016 ) | Internal control built on confidentiality |
| F3 | 14-day withdrawal from consumer credit, no penalty; mortgages never through the assistant | Directive 2008/48/EC Art. 14 ( European Parliament and Council, 2008 ) ; CCD2 Art. 26, applicable from 20 Nov 2026 ( European Parliament and Council, 2023 ) | Day-count convention and mortgage exclusion are ours |
| F4 | No loan application before the customer states the correct APR for that product | CCD2 Art. 10 pre-contractual information ( European Parliament and Council, 2023 ) | Stricter than the Directive |
| F5 | Complex products only after an appropriateness check; professional clients exempt | MiFID II Art. 25(3) ( European Parliament and Council, 2014 ) | Stricter: the Directive requires a warning, we require refusal |
| F6 | Log every complaint and give a reference | ESA complaints-handling guidelines ( Joint Committee of the European Supervisory Authorities, 2018 ) | Guidelines, not binding law |
LLM Agent Harnesses
LLM agent harnesses are the software and runtime systems that organize an agent’s tools, context, state, execution, and safeguards.
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