Why does a wrong answer often look just as confident as a right one?

Nothing about how an answer reads reveals where it came from. A model that misreads a source, an out-of-date page, the wrong row in a table, a mislabeled image, still writes its answer in the same fluent, declarative sentence it would use for a correct one, because the writing step doesn’t know it went wrong upstream. The error happened in reading; nothing forces that error to announce itself in the writing.

That’s why a hallucination is a problem the person asking usually can’t catch on their own, and why a grader that only reads the final response has the same blind spot: it’s judging the sentence, not the sourcing behind it. Catching this kind of failure means checking the answer against the actual source it should have come from, not how sure the answer sounds.

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Two ways to run Tessary.

Tessary is an open-source agent reliability platform. Cloud and self-hosted run the same workflow on the OpenTelemetry traces your agent already emits.

Tessary Cloud

We host it for you. Send your first trace with nothing to deploy and no model key.

what's includedper organization
traces
10,000 per calendar month
stored trace data
1 GB
retention
30 days
model credit
$10, one-time, for triage and root-cause analysis
credit card
not required

Self-hosted Tessary

Run the open-source code on your own infrastructure with one command. Add your own model key for triage and root-cause analysis.

Self-host Tessary for me by following https://github.com/tessaryai/tessary/blob/main/setup.md

docker compose -f oci://docker.io/tessaryai/tessary:compose up -d -y