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This position paper aims to formalize deployment patterns for AI agents in early-stage wellness platforms.

April 23, 2026Open Access

Ship It Broken, Label It Honest: Deployment Patterns for AI Agents in Early-Stage Wellness Platforms

Key Points

This position paper aims to formalize deployment patterns for AI agents in early-stage wellness platforms.
Identified six deployment patterns: experimental labelling, stability-first model selection, data gap architecture, progressive onboarding, WHO QoL anchoring, tiered AI credits.
Utilized Bhaskar's critical realism framework to evaluate implications of these patterns.
Proposes that labeling AI outputs as experimental influences user perception and engagement.
Advocates for stability-first model selection to prioritize reliability over novelty in AI outputs.
Establishes a tiered system for AI credits, suggesting varying levels of AI engagement.

Abstract

Position paper formalising six deployment patterns for AI agents in wellness platforms: experimental labelling, stability-first model selection, data gap architecture, progressive onboarding, WHO QoL anchoring, and tiered AI credits. Grounded in Bhaskar's critical realism, the paper argues that labelling AI outputs as experimental is an epistemological commitment, not a UX compromise.

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