When an AI agent does something wrong, the first question is never "how do we make it faster?" It is "what exactly happened?"
And most of the time, nobody can answer it. The agent ran, something went out, and the only trace is a vague memory of what someone asked it to do.
That is why automation that lasts starts with a record, not with speed.
An evidence log keeps two kinds of facts apart:
Mixing them is the most common mistake. "The agent said the cost rate is 28%" is a conclusion. "The agent read invoice #412, used the tax-excluded price, and divided by the usable quantity" is evidence plus state — and it is the only form you can actually check.
When the answer looks wrong, you do not re-argue with the agent. You open the log, look at the evidence first, and find where it went off. In most cases the arithmetic was right and the material was wrong.
You do not need a full observability platform. A small record per run is enough:
Three rules keep the log useful:
Automation projects usually fail in the same order: first something goes wrong quietly, then nobody can explain it, then the whole thing gets switched off — not because it was useless, but because it was unexplainable.
A record breaks that chain. When something goes wrong, you can point at the exact step, fix that step, and keep going. The people who keep automating are not the ones with the best tools. They are the ones who can say what happened last time.
After every automated run, keep one line: what ran, what it used, what came out. That is the whole habit. Speed comes later, once you can trust what the record says.
Measure first, then automate faster. The record is what makes the measurement possible.