From POC to Production: Making AI Agents Deliver Real Business Value in Retail

Retail digitalization has evolved through ERP systems, online platforms, microservices, and cloud adoption, now entering the complex phase of AI Agent implementation.

Generative AI demos impress but often fail in real production due to business complexity, data security, cost control, and multi-cloud constraints.

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ITPUB interviewed Yang Jing, Technical Deputy General Manager at 本来生活集团, on retail digital transformation, AI Agent product deployment, R&D efficiency, and cloud architecture evolution.

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Traditional software projects prioritize deterministic delivery, while AI Agent projects face output uncertainty. Yang emphasizes that increasing AI execution speed doesn't reduce human judgment responsibility at critical nodes.

To escape the "heavy demo, light production" trap, AI Agents must integrate into grocery retail workflows to deliver stable, controllable business value.

Yang's core judgment: business scenarios must drive AI development, not the reverse. For most mid-sized retailers with weaker digital foundations, starting with specific scenarios is more practical than buying large model platforms.

Three work patterns emerge: FDE (front-end engineers) for long-tail small needs, Loop teams for cross-department collaboration, and AI SDD (Spec-Driven Development) for architecture governance.

Technical debt management requires making debt visible, classifying by urgency, and establishing fixed governance rhythms. Architectural platforms must converge capabilities, control tool orchestration, and standardize component access.

Full-chain observability provides evidence for governance decisions, tracking module alarms, dependency failures, and business impact scope.

Yang's three architectural principles: fitness over perfection, conservative middleware selection, and always maintaining rollback plans. Architecture must be validated by business outcomes—delivery speed, stability, and cost control−not diagram aesthetics.