AI apps convert trials 52% better and churn annual subscriptions 30% faster. The flattering metric lands in week one. The honest one lands in month twelve and by then you've already spent money on the gap.
Here's a pairing I can't stop thinking about, and both halves come from the same dataset.
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?
In this time when we use LLM's for basically anything, what AI agents/tools would you want that you would actually pay for? What are you spending a lot of money on and would probably save a lot of you time if you got the Agent out of box, or if configuration was much easier?
