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Building AI Support Agents That Cannot Overstep Their Own Boundaries

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A software developer has documented a 14-part experiment building an LLM-powered customer support agent designed with strict architectural boundaries that prevent the AI from making consequential decisions on its own. The system lets the AI handle intent recognition and document retrieval, while deterministic software code controls eligibility checks, risk tiers, and user scoping. High-risk actions are either queued for human approval or can only be proposed — never executed — by the AI. Automated architecture tests enforce these boundaries at the code level, so importing a framework into core business logic causes the build to fail immediately. The developer openly distinguishes between deliberate design trade-offs and unfinished work, arguing that honest documentation of gaps is essential to maintaining the integrity of AI system design.

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Building AI Support Agents That Cannot Overstep Their Own Boundaries · ShortSingh