Agentic Evals Use Cases
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Practical, end-to-end walkthroughs for scoring agents and gating their releases. Every use case below was run against agentic-evals 0.9.0 from PyPI and lists the exact versions it was tested with. None of them needs a model, an API key or a network connection.
Catalog
| Use case | Summary | Difficulty | Capabilities | Verified version |
|---|---|---|---|---|
| Block a release when one safety case fails | A faster candidate passes 5 of 6 cases, clearing an 80% floor. A per-tag floor still blocks it, because the one failure is a refund it was never allowed to make. | Beginner | Declarative CLI, Tag breakdowns, Release gates | 0.9.0 |
| Catch a right reply reached the wrong way | Four refund runs send the same reply and pass every text check. Their traces show a skipped identity check, a $499.90 refund and a loop. | Intermediate | Tool expectations, Trajectory scorers | 0.9.0 |
| Don't let a flaky LLM judge approve a release | Judge each answer three times with retries. A timed-out judgment, an undecided judge and incomplete judge cost each fail the gate. | Intermediate | Judge reliability, Rubric scorers | 0.9.0 |
The three cover a CI gate you can copy today, scoring what an agent did and not only what it said, and keeping an LLM judge honest.
Verified package version
- Package version: agentic-evals 0.9.0 from PyPI
- Environment: Python 3.12.3 on Linux, base dependencies only
- Verification date: 2026-10-26
- Scope: every command and script in the walkthroughs was run and its output
copied into the page. The same examples run in this repository's test suite
(
tests/test_usecases.py), so a change that alters their output fails CI.