Dev builds AI sanctions-screening tool for NGOs, uncovers critical error-handling flaw

A developer built an AI agent called Interdict to automate OFAC sanctions screening for small NGOs, which face the same compliance exposure as major financial institutions but lack resources to manage it. In its first real test run using Google's Gemini model, the system quarantined 438 of 536 counterparties — flagging them for human review. The root cause was not a model failure but a coding bug: API rate-limit errors were misclassified as model-integrity failures, flooding the compliance queue with false escalations. This buried genuine cases requiring human attention under hundreds of irrelevant entries, effectively turning a safety signal into noise. The fix involved distinguishing transient network errors from true model failures and implementing proper retry logic with server-guided backoff.