As AI systems take on more consequential tasks, instructions alone are not a sound place to put the limits on what they can do. In a refund-support design described by Antonio Lopes Correia, the model interprets a request and finds relevant documents, while application code controls customer access, checks eligibility, assigns risk, and routes cases for human review. The model helps with the work; the surrounding system retains authority over the outcome.
What the refund-support agent lets AI do
The example is an LLM-powered customer-support agent for refund requests. Its model is responsible for interpreting customer intent and retrieving relevant documents. It does not decide who the customer is, determine whether that customer qualifies for a refund, or execute the refund.
Instead, authenticated customer identity scopes the document lookup, and application software applies refund eligibility rules and assigns a risk tier. Medium- and high-risk cases go to a human approval queue; the model does not make the final high-risk decision.
That division is the architectural point: the model can help understand a request, but deterministic application logic and people retain the consequential decisions and actions. This is one described design, not evidence that any particular architecture guarantees reliable or safe AI.
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Why an enforced boundary is different from an instruction
A prompt can tell a model not to perform an action, but an instruction is not the same thing as a technical control. The example adds a build-time dependency allowlist around business-rule code: the policy class may depend on itself and the Java Development Kit (JDK), while adding a JSON library to that class makes the build fail.
This check constrains what the rule code can depend on. It does not prove that every policy is correct or every possible failure is handled, but it makes one architectural boundary enforceable during builds rather than leaving it as a convention in a prompt or README.
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Which omissions were deliberate—and which were unfinished?
“What’s the gap in your system that everyone calls deliberate?” is a useful question because an intentional design choice and a missing safeguard are not interchangeable. In Correia’s account, some omissions were made to keep authority out of runtime configuration; other protections remained work to be done.
Deliberate design omissions
- Per-tenant risk policies: the author left these out rather than make risk tiers runtime-configurable. The stated concern is that configuration would then hold authority over a consequential rule.
- Durable queue and audit storage: the described queue and audit trail use in-memory adapters. Their interfaces indicate what a production adapter must provide, but persistence is not implemented in this example.
- LLM-as-judge evaluation: the author excluded a language model as evaluator because the reliability of that judge would first need to be established.
Safeguards still unresolved
- Proposal rate limits: the project does not rate-limit refund proposals.
- Duplicate execution after retries: it does not prevent the same approved refund from being executed twice when a retry occurs.
- Knowledge-base degradation: it does not detect when the knowledge base degrades.
These unresolved items matter because a boundary around who may decide is only part of a dependable system. A real service also needs controls for repeated actions, excessive proposals, and deteriorating information. The account does not describe those controls as implemented.
Rank #3
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What the evaluation does—and does not—show
The project had not served a real customer, and its evaluation suite consisted of scenarios invented by the author. Passing those cases can show repeatability on the cases tested; it cannot establish how the agent behaves with real users, production traffic, or failures absent from the test set.
The comparison described was between components, not AI providers. End-to-end latency with a hosted model was not measured. The account supplies no attributable statistic quantifying a gain in safety, reliability, latency, or cost, so it supports a qualitative architectural argument rather than a numerical claim about outcomes.
Rank #4
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How to assess the architecture of an AI feature
When reviewing a system that gives AI access to important workflows, trace the boundary from request to action rather than judging it by prompt wording alone:
- Identify the model’s role. Is it interpreting, summarizing, or retrieving—or does it also decide eligibility and trigger an irreversible action?
- Locate the authority. Check whether code or a person owns identity scoping, policy decisions, risk handling, and approval. Look for controls that can be enforced, not just instructions that describe intended behavior.
- Separate intentional exclusions from missing safeguards. Record what was intentionally kept out and why, then list unresolved risks such as retries, rate limits, or data-quality monitoring separately.
- Check the evidence behind reliability claims. Distinguish invented test scenarios from observed customer behavior, and component comparisons from end-to-end measurements.
Correia’s central warning is that increasing capability makes the location of authority more important, not less: “The more powerful the AI becomes, the less you can let the AI itself define the boundaries.”
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