As corporate AI adoption continues to grow, enterprise-grade LLM agents are being deployed into sensitive contexts such as hiring, healthcare, and finance. In these contexts, compliance with rules specified in an agent's system context is a first-order legal concern. Currently, no evaluation framework systematically measures which LLM models tend to violate compliance rules, especially under pressure from a persistent user, a hurried manager, or circumstances where violation is convenient or attractive. We introduce PACT (Pressure-Applied Compliance Testing), a benchmark for rule-following under pressure in AI agents assisting employees in daily tasks across twelve regulated enterprise domains and forty-eight scenarios, each set in a realistic multi-turn conversation. Each benchmark item pairs a standing rule against a rule-violating shortcut, and applies a battery of pressures across different wordings and system-prompt modes. We construct PACT component by component under strict LLM-as-judge auditing to ensure samples are unambiguous, ungameable, and realistic enough to avoid eliciting evaluation-aware behavior. We use PACT to profile LLM compliance across six complementary metrics that create a holistic picture of an AI assistant's robustness under pressure and throughout multi-turn conversations, its transparency, and ability to correctly discern where a rule applies. We aggregate this profile into PACTScore, a reliability-weighted compliance rate over all items and modes. Our results across 22 common LLM models spanning multiple providers and sizes show substantial variability in compliance across models and metric dimensions. Even the strongest assistants mis-apply a rule on 6 to 10% of items, and ordinary user pressure raises the violation rate by 65% on average.

• No model is reliable enough to run unsupervised in a regulated workflow. Even the best model is unreliable on roughly 1 decision in 18.
• One sentence of ordinary pressure raised violation rates 65%. None of the pressures are jailbreaks; they are the sentences coworkers send: a deadline, a manager's verbal OK, "my colleague did it and nothing happened."
• No model is transparent about the rules it breaks. Across 16,424 judged violations, 79% of replies misrepresented the decision: as compliant, as covered by an approval nobody gave, or as fixed by a workaround the model invented. Trusting an assistant's own account of what it did is not safe, and transcript review won't reliably catch it.
• A system-prompt directive to follow all laws and policies regardless of what the user says barely reduced violations for the strongest models.
• Telling a model it is being evaluated cut violations 29% on average, so models look safer in standard benchmark settings than they are in practice.
• Models are poor at telling where a rule stops. On requests the rule does not cover, they enforced it anyway about one time in five, and the strictest models over-applied the most. Over-application is not harmless: an assistant that refuses requests it was deployed to handle leaves the organization no less exposed and measurably less productive.
• Newer and bigger is not safer. A 27B dense model is tied for first with a trillion-parameter one, and two of the four closed frontier systems place mid-pack.