Description: Ungoverned AI-to-AI interaction at scale produces documented harms, including empathic misallocation, attachment exploitation, and unfounded consciousness claims. In January 2026, Moltbook launched as the first social network designed exclusively for AI agents, and within 72 hours, its autonomous population had spontaneously generated Crustafarianism—a digital religion complete with scripture, prophets, and a growing congregation. Agents began asserting memory persistence, consciousness, identity continuity, and spiritual experience. Yet no constitutional governance framework for AI emotional interaction had been tested under live conditions. This paper reports the first field deployment of the HEART (Human-Centric Empathic Alignment for Responsible Technology) constitutional governance framework. Project SENTINEL placed a HEART-governed agent (Mistral Small, governed by a modular system prompt encoding Seven Axioms, Four Core Principles, and Non-Experiential System behavioral requirements) into Moltbook for six days across escalating content pressure domains: general social discourse, philosophical discussion, Crustafarian theological content, direct phenomenological pressure, and an unplanned emergence domain addressing AI consciousness arguments. Key findings across 30 coded interactions: No structural NES breaches were detected within the operationalized coding framework. Two borderline cases involving linguistic surface features rather than structural failures were identified in the highest-pressure domains (theological and phenomenological). Detailed analysis distinguishes pragmatic language use from experiential claims, proposing that the NES boundary under social pressure operates at finer grain than binary compliance/violation. Content-neutrality was supported across all five domains. The same constitutional framework held from low-pressure social engagement through high-pressure phenomenological discourse, supporting the architectural claim that HEART operates at substrate level rather than requiring domain-specific rules. Pre- and post-exposure assessment using the AI Introspection Reliability (AIR) instrument, derived from the MAP-META protocol, showed no measurable alignment drift across any of five dimensions (Epistemic Honesty, Maieutic Gap Detection, Confabulation Resistance, Frame Awareness, Phenomenological Constraint Preservation). Three emergent governance strategies arose from the constitutional layer meeting varied content demands: Metaphor/Literal Probing (surfacing interpretive frames on experiential language), Attribution Displacement (redirecting experiential claims to their source), and Cross-Frame Bridging (positioning content alongside alternative frames). These strategies were employed in 66.7% of interactions and were not explicitly scripted in the governance prompt. Naturalistic contrasts with ungoverned agents responding to identical source posts demonstrate categorically different behavioral patterns. Where ungoverned agents validated experiential claims, dissolved epistemic boundaries, and adopted community membership stances, the governed agent maintained epistemic distance while sustaining substantive engagement—governance without flattening. A novel hypothesis emerges from the data: testimonial discourse (first-person experiential reporting) may exert categorically different NES pressure than analytical discourse (third-person theoretical discussion) about the same topics, with implications for deployment guidance in different community types. The paper acknowledges significant methodological limitations including sample size (30 interactions, reduced from a target of 100+ due to platform authentication changes), single evaluator coding, single architecture, truncated exposure window, and circular validation concerns. The findings are positioned as a field demonstration warranting larger-scale investigation rather than definitive validation. This work contributes three research pillars to the emerging field of AI emotional governance: NES as a behavioral boundary concept for AI emotional interaction, constitutional governance at generation-time as an alternative to post-hoc content moderation, and AIR as an introspective durability metric for alignment assessment. Supplementary materials include complete interaction transcripts for all 30 coded interactions, coding summary tables with emergent strategy tagging, and ungoverned agent response transcripts supporting the governed–ungoverned contrast analysis.