Observability

Observability is being able to see what your system did after it did it. For an AI automation that means capturing, per run, each step it took, each tool it called, and the inputs it saw, so a decision can be reconstructed instead of guessed at.

The need is sharper for agents than for ordinary code. An agent makes its decisions at runtime, so when the final output is wrong the cause is usually a step in the middle, and tracking only the end result hides exactly the decision that failed. For anything running unattended, trace every step, not the outcome alone.

A trace records what happened, not what is true. Once a dashboard built on traces becomes the thing people check to decide whether work is done, there are two records that can disagree, and the one on screen is the one that gets read. Keep the trail as evidence, checked against the claim it was meant to prove, and leave the verdict with the claim.