Albert Nelson

I noticed a small problem in my debugging workflow with AI agents.

When something broke, my first instinct was:

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?

After being on Product Hunt for almost 4 years, I ve realised one thing.

The vast majority of people launch their product once, and that s it.

It gives me a slight feeling that the product might stop existing a few months later.

On the other hand, makers who repeatedly launch major updates give me the impression that they re serious about their product s direction.
I d probably feel much more comfortable paying for a lifetime deal from them because the product is likely to still be around months from now.