Yuhao Lin (developer) conducted a controlled experiment to evaluate how rule-setting formats influence AI agent behavior. The study involved 6 sessions and 150 standardized tasks executed by DeepSeek V4 Pro. The tasks were divided into two conditions: syllogism-based rules, which frame constraints as causal chains, and imperative rules, which use direct commands. Each task type spanned config editing, design decisions, builds, debugging, and documentation. While the researcher initially hypothesized that syllogism-based framing would result in fewer errors, both conditions achieved a 99.3% compliance rate, with 149 of 150 tasks showing no rule violations.

The unexpected high compliance was primarily attributed to the presence of GateGuard, a mechanical verification system that blocked unverified edit and write operations. Before implementing GateGuard, previous logs indicated a 55.9% rule violation rate across 34 sessions. After wiring the mechanical gate, that figure dropped to 0.7%. This result suggests that mechanical enforcement of constraints is significantly more effective than semantic phrasing in ensuring agent adherence to rules.

Despite the lack of variance in compliance, the experiment revealed differences in reasoning. Syllogism agents anchored their actions in causal chains and introduced multi-perspective cross-reviews during complex design tasks where GateGuard did not intervene. In contrast, imperative agents relied on checklist-style outputs. The author noted limitations such as self-scoring biases and potential filesystem pollution, suggesting that future research requires human raters and GateGuard-disabled test cases to isolate format effects.

Yuhao Lin (developer) conducted a controlled experiment to evaluate how rule-setting formats influence AI agent behavior. The study involved 6 sessions and 150 standardized tasks executed by DeepSeek V4 Pro. The tasks were divided into two conditions: syllogism-based rules, which frame constraints as causal chains, and imperative rules, which use direct commands. Each task type spanned config editing, design decisions, builds, debugging, and documentation. While the researcher initially hypothesized that syllogism-based framing would result in fewer errors, both conditions achieved a 99.3% compliance rate, with 149 of 150 tasks showing no rule violations.

The unexpected high compliance was primarily attributed to the presence of GateGuard, a mechanical verification system that blocked unverified edit and write operations. Before implementing GateGuard, previous logs indicated a 55.9% rule violation rate across 34 sessions. After wiring the mechanical gate, that figure dropped to 0.7%. This result suggests that mechanical enforcement of constraints is significantly more effective than semantic phrasing in ensuring agent adherence to rules.

Despite the lack of variance in compliance, the experiment revealed differences in reasoning. Syllogism agents anchored their actions in causal chains and introduced multi-perspective cross-reviews during complex design tasks where GateGuard did not intervene. In contrast, imperative agents relied on checklist-style outputs. The author noted limitations such as self-scoring biases and potential filesystem pollution, suggesting that future research requires human raters and GateGuard-disabled test cases to isolate format effects.