Your automation's dashboard is green. That is not the same as it being right.

When a workflow crashes you find out inside the hour. When it quietly returns the wrong answer — an invented order number, a half-finished write, a stale document cited confidently, a price nobody authorised — it keeps running, and everything downstream inherits the mistake. No alert fires, because nothing malfunctioned.

I read what your system actually produced and tell you where it is quietly wrong.

You send a sample of real outputs and a description of the workflow. No account access, no credentials, no integration. I work through a 16-point failure taxonomy covering model errors (schema drift, invented identifiers, stale retrieval, silent truncation), plumbing (duplicate execution on retries, partial multi-step writes, empty results read as "nothing to do", unbounded loops), drift (model version changes, locale and encoding, time zones), and the failures with a customer on the other end (instructions arriving inside ingested data, commitments made on your behalf, personal data reaching somewhere new).

You get up to 10 findings, each one proved with the actual output that demonstrates it, ranked by what it costs you — plus a set of adversarial test cases you keep and re-run yourself every time you change a prompt or a model.

The full taxonomy is published and free to read before you buy, no sign-in needed — https://koraynar.github.io/research-samples/silent-failures/

The same evidence approach applied to a paid-ads account, every figure shown against its source — https://koraynar.github.io/research-samples/missing-ledger/