Approving a $50,000 wire is the textbook example of work AI shouldn't do alone.

Wire transfer approvals follow a general structure, but each instance involves unique counterparties, contexts, fraud signals, and business relationships that require fresh evaluation. The structural similarity is superficial; the judgment required varies significantly case by case.

Success criteria sound simple — approve or deny — but the actual decision depends on nuanced fraud detection, compliance checks, business context, and risk tolerance that are rarely fully codified. An agent cannot reliably know when it has 'done this right' without human validation.

An agent could potentially access transaction data, account history, and fraud scoring APIs, but critical context — verbal confirmations, relationship history, internal business intent — often lives outside any system the agent can reach. Incomplete data access in a high-stakes financial decision is disqualifying.

A wrongly approved wire transfer can result in immediate, largely irreversible financial loss, potential fraud liability, and regulatory penalties. A wrongly denied transfer can breach contracts or damage critical business relationships. Both failure modes carry severe, real-world consequences.

This decision carries fiduciary, legal, and ethical weight that regulators and courts assign to accountable humans, not automated systems. Fraud detection at this level also requires contextual intuition — reading anomalies, verifying intent — that current AI agents cannot reliably provide.