A customer-support AI can interpret a refund request without being allowed to approve or issue the refund. In a design account published by Antonio Lopes Correia on September 19, 2026, the model handles language and document retrieval while application code scopes customer data, checks eligibility, assigns risk, and routes consequential cases to human review. The approach illustrates a useful principle—not a guarantee: enforce important boundaries in the system, rather than trusting model instructions alone.
What the AI does—and what the application controls
The example is a support agent for refund requests. Its model interprets what a customer is asking and finds relevant documents. The rest of the decision path belongs to the surrounding application:
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- It scopes lookups to the authenticated customer.
- It determines whether a request meets refund eligibility rules.
- It assigns a risk tier.
- It sends medium- and high-risk cases to a human approval queue.
The model does not make the final decision on high-risk requests or execute refunds. This division matters because a prompt can ask a model to follow a policy, but it does not itself enforce that policy. The application can make access and action depend on code paths that the model cannot simply redefine.
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One concrete safeguard in the design is a build-time dependency allowlist around business-rule code. The policy class can depend on itself and the Java Development Kit (JDK); adding a JSON library dependency to it causes the build to fail. That check helps keep infrastructure concerns out of the rules that determine business outcomes.
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This is different from writing “do not use a JSON library” in a prompt or README. The allowlist makes a structural constraint testable during the build. It does not, by itself, prove that every rule is correct or that the whole system is safe; it helps enforce one specific boundary.
Deliberate omissions are not the same as unfinished safeguards
The design account distinguishes choices made to keep authority in the application from controls the project has not yet implemented. That distinction is useful when asking the source’s question: “What’s the gap in your system that everyone calls deliberate?”
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Choices made deliberately
- Risk policy is not tenant-configurable. The author left out per-tenant risk policies because runtime configuration of risk tiers would place authority over a consequential rule in configuration.
- No LLM-as-judge evaluation. The author excluded using another language model to grade the agent because the judge’s own reliability would first need to be established.
- Persistence is represented by interfaces, not durable adapters. The queue and audit trail use in-memory adapters. The interfaces describe what a real adapter must provide, while durable persistence is omitted from this example.
Unfinished safeguards
- The system does not rate-limit proposals.
- It does not prevent an approved refund from being executed twice after a retry.
- It does not detect degradation in its knowledge base.
These are unresolved gaps, not hidden features of the design. In a consequential workflow, labeling them plainly makes it easier to decide what must be completed before deployment.
What the evaluation can—and cannot—show
The project has never served a real customer, and its evaluation scenarios were invented by the author. Passing those cases can show repeatability against that particular set, but cannot establish how the agent will behave with real users, production traffic, or failures the author did not anticipate.
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The account also does not provide a measured improvement in safety, reliability, latency, or cost. Its component comparison is not a comparison between model providers, and end-to-end latency with a hosted model was not measured. Treat it as a design account, not a controlled study or a numerical ranking of architectures.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.How to assess boundaries in another AI system
When reviewing an AI application that can affect money, access, or customer records, examine where authority actually sits—not only what its instructions say. Useful questions include:
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- Which decisions are made by deterministic application code, and which are delegated to the model?
- Can the model access only the authenticated user’s records, or does the application enforce that scope?
- Who sets eligibility and risk rules, and can runtime configuration alter them?
- Do high-consequence cases require human approval, and can the model bypass that path?
- Are structural constraints checked automatically, such as through build-time dependency rules?
- Which gaps are explicit design choices, and which remain unimplemented safeguards?
- Does validation use invented scenarios, observed customer behavior, or both—and what does that evidence actually establish?
Antonio Lopes Correia’s central point is that capability makes boundary design more important, not less: “The more powerful the AI becomes, the less you can let the AI itself define the boundaries.” The refund example gives that claim a practical shape, while its unimplemented safeguards and limited evaluation make clear why architectural controls still need testing and follow-through.
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