Does a regression grader have to be an LLM judge?

No, and most of the time it shouldn’t be. A regression grader exists to catch one specific failure that already happened once, and a deterministic check, code that asserts the exact condition that broke, a status code, a missing field, a string that shouldn’t appear, is cheaper to run and doesn’t drift the way a judge’s reasoning can drift between model versions.

An LLM judge earns its cost when the failure isn’t a fact you can assert in code but a matter of meaning: the agent technically answered but missed the point, or contradicted something said earlier in the conversation. That kind of failure needs a model reading the trace and reasoning about it, because no regex or field check can tell you the answer was off topic.

Write the deterministic check first. Reach for a judge only when the check keeps passing on traces you know are still broken.

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More on this.

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