We build a platform that finds waste. Then we found a pile of it in our own agent.

We tell people the same thing every day: the waste isn’t where you’re looking. It’s hiding in the places nobody thinks to check.

Then we caught ourselves doing it.

We stream Sherpa’s answers back to you word by word. And we found that from the second reasoning step onward, the framework behind our agent was re-sending the entire prior conversation inside every streamed chunk — every earlier message, every tool call, again and again. The payload grew quadratically with each step. One heavy, multi-step session was pushing roughly 22MB down the stream that the screen never even rendered.

Twenty-two megabytes of pure dead weight. On something we’d already shipped. On something we were proud of.

Here’s the uncomfortable truth about AI tooling right now: most of it is quietly bloated. It works, so nobody looks. The cost hides — in latency, in compute, in a bill that creeps up a few points a month while everyone shrugs.

That’s the exact disease Sherpa exists to cure. Finding it in our own stack only sharpened the thesis:

If a cloud platform isn’t ruthless about its own efficiency, why would you trust it with your bill?

We stripped the echo, and the stream got lean. And we kept the lesson taped to the wall: the waste is always closer than you think.