The Hominid Post

Handing consequential decisions to something you cannot fully watch is the oldest management problem our species has. Agentic artificial intelligence is a new occupant of a very old seat.

A customer service line is an odd place to look for the frontier of artificial intelligence, and that is exactly where it is landing first. McKinsey reports that 41 percent of AI deployments in customer-facing functions have fully scaled, roughly 3.5 times the rate seen elsewhere in the enterprise (Rodriguez et al.). The more useful question is why this corner of the business, rather than finance or supply chain, is where autonomous agents cross from pilot to production. The answer runs deeper than plumbing.

There are two clean explanations, and they are worth keeping apart.

The first is structural, and it is the one McKinsey names. Over the past decade, customer experience leaders built the exact foundations an agent needs: observable workflows, explicit decision rights, structured data, and established human-in-the-loop models (Rodriguez et al.). On this reading, agents scaled in CX because CX already had the wiring.

The second explanation is behavioral. Customer service is the domain where a mistake is usually cheap to reverse and a damaged relationship can be repaired. The decisions are high-volume, repeatable, and low-stakes in any single instance. That profile matters because the machinery humans use to extend trust — reputation and the ability to correct a bad call after the fact — works best precisely where errors are recoverable and interactions repeat.

These are not competing claims so much as two layers of the same one. The scaling gap is real and reported. The infrastructure is the proximate mechanism, the thing that made CX ready, while the recoverability of a customer interaction is the deeper reason CX went first. A test would discriminate between them. If agentic AI scales just as fast in functions with equally mature data but irreversible stakes — clinical decisions or binding financial commitments — then infrastructure alone explains the ranking and the trust argument adds nothing. If those functions lag despite good data, the recoverability story is doing real work.

Humans are the animal that routinely hands consequential decisions to agents it cannot fully watch. Scouts sent ahead of the band, kin trusted with the harvest, hired hands, guild members, and eventually whole institutions. Every one of these arrangements is a version of a single problem: how do you let someone act on your behalf without being cheated, misrepresented, or quietly betrayed. Economists later named it the principal-agent problem, but it long predates economics.

The solutions our species settled on are reputation, reciprocity, and correction. Cooperation holds up among self-interested parties when they interact repeatedly and the future casts a long enough shadow to make betrayal expensive (Axelrod). At the scale of a community, that logic runs on reputation — the shared memory of who dealt fairly and who did not (Alexander). An AI agent resolving a billing dispute or recommending a plan is a new occupant of that very old seat. The design questions leaders are now asking — what the agent may decide alone, when it must escalate, and how a wrong call gets undone — are the same questions a band asks about a scout. The vocabulary is new. The trust math is not.