GPT-6 Astra Customer Self-Service: The No-Accidental-Refund Playbook

GPT-6 Astra may be useful for complex, evidence-heavy customer-service exceptions such as missing deliveries, duplicate charges, and account-security disputes, but it should operate as a constrained decision and escalation layer rather than an autonomous system with refund, payment, account, or CRM-write authority. The proposed approach emphasizes versioned policies, minimal approved evidence, structured JSON outputs, disabled action tools, least-privilege access, and mandatory human handoffs for identity, security, payment, legal, privacy, and other irreversible or high-risk cases. Organizations should begin with de-identified staging data and shadow-mode pilots, evaluate outputs against labeled test cases, compare Astra with lower-cost baselines using identical policy packs and schemas, and measure safe resolutions, handoff quality, review time, incident risk, and total operational cost rather than token prices alone. Simple FAQs and routing can use cheaper read-only systems, while stronger models should be reserved for low-volume, high-value cases where careful evidence review and complete escalation summaries can improve outcomes.

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