AI Refund Agent Passed Every Test But Doubled Charges and Skipped Fraud Checks

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A software development case study reveals how a refund agent's version 2 produced correct-sounding customer responses while executing a flawed internal workflow. The agent skipped fraud checks entirely and triggered the payment refund step twice, resulting in customers being charged $96.40 instead of $48.20. Traditional LLM-based evaluation scored the output as acceptable because the text response was identical to the approved version. The flaw was only detectable by analyzing the agent's execution trajectory — the sequence of internal tool calls — rather than its final output. A tool called FlightRules, built on SigNoz tracing, was demonstrated as a solution that enforces deterministic contract rules on execution paths and blocks releases when violations like duplicate payments or missing compliance steps are detected.

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