Lazar J

The models got dramatically smarter this year. Your product didn't get more trustworthy and in 2026, those turned out to be two completely different problems.

Here's the line that reframed it for me. Stanford's AI Index this year leans on a phrase from Ethan Mollick the "jagged frontier" to describe how these systems excel and then fall off a cliff with no warning. Their example is almost too good: an AI model can win a gold medal at the International Math Olympiad and still not reliably tell time from a clock face. Genius and incompetence living in the same box, and no obvious seam between them.

Everyone read today's pricing news as costs going down. On agent workloads it doesn't work like that.

A cheap model that gets something wrong rarely fails loudly. It returns something plausible, the next step eats it, and you find out four steps later. By then you've paid for the cheap call, the three calls after it, and the rerun on the expensive model. A call that errors out is nearly free by comparison, because you just retry it.

Hey Product Hunt community!

As autonomous AI agents move from experimental chat interfaces into deep production workflows, they are being granted direct access to critical enterprise APIs and execution environments. While the industry is heavily focused on model accuracy and latency optimization, I believe runtime security remains a massive architectural blind spot.

Traditional API gateways and static firewalls fall short when dealing with semantic prompt injections hidden inside natural language payloads often leading to unauthorized API executions or silent PII leaks.