Oyeyemi Akinrinade

Imagine an AI agent responsible for routing inbound leads. Nothing crashes. No alerts fire. The workflow keeps running exactly as expected.

The problem is that the agent has slowly started sending high-value leads to the wrong queue. Maybe a few customer issues are being summarized inaccurately. Maybe records are being updated with small mistakes that seem harmless on their own. Each individual error is easy to miss. Over time, though, the impact starts to compound.

Those are the AI failures that interest me most because they rarely look like failures at first. There is no outage, no red warning message, and no obvious signal that something is wrong. The workflow continues operating, but the quality of the outcomes quietly drifts away from what the team intended.

That makes detection a different challenge altogether. It's less about system uptime and more about observation. Are there feedback loops? Quality checks? Escalation paths? Can someone spot a pattern before customers, revenue, or operations start feeling the effects?

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My entire career has been about one thing: protecting and serving customers. IT security, systems design, lifecycle support, customer service every role was customer-facing. I learned that trust isn't built in the backend. It's built in every interaction.

But I also saw a broken pattern. Growing businesses were being priced out of great customer support. Enterprise tools with enterprise price tags. Per-seat taxes. Hidden AI markups. It felt like the industry was punishing companies for not being big enough.