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Budgets, limits and evidence

Use the live launch form and readiness messages for the active environment’s policy. These are ceilings and evidence boundaries, not security guarantees.

The chosen depth controls admitted work. The examined Light profile defaults to ceilings of 30 target calls, 100 model calls and 360 seconds; organization/server policy can reduce or reject that request. The interface, target authorization and compatible execution mode can further limit concurrency, supported checks and reproduction.

Cancellation requests a stop. It does not undo a request already sent or prove a provider has physically finished. Inspect final state, skipped/failed checks and retained evidence.

Area Current boundary
Target-call budget API 1–1,000 selected records; UI choices 100 / 250 / 500 / 1,000
Small tutorial Use a three-record dataset: budget 100 is an upper bound, not 100 mandatory requests
Concurrent target runs One queued/running evaluation per organization, across projects
Concurrent scoring jobs One active scoring job per organization, separate from target admission
Target execution Sequential; ACTIVE usable records within the selected budget
Human queue 1–100 selected results, 1–8 unique required questions
Bulk dataset/review actions Up to 100 selected records per action
Trace query page Up to 100 rows; default 25
Bounded trace exports/scans Limits apply at 1,000 matches; narrow filters when a complete export is refused
Dataset import Up to 20 MiB and 100,000 records; the whole invalid upload is rejected

Stop after current request retains an in-flight result, prevents the next target call and marks remaining selected records skipped. Scoring stop similarly retains the in-flight judgment. Reopening, exporting or reviewing saved values does not execute the target again.

Canonical exact match compares stripped, case-folded actual and expected strings. Punctuation and extra text still matter. Other scores preserve their own definition and type. Numeric/category/text feedback must not be presented as a Boolean pass rate. Missing token or cost data is unknown, not zero.

Published dataset versions and runs retain pinned identities/digests. A declared agent artifact revision is useful provenance; an agent’s name cannot establish its model or prompt version. Historical data and bounded runtime observations do not independently verify effective access, predict an incident or prove a loss ceiling.

A clean reproduction, negative control, independent business check and model opinion establish different things. Read assessment evidence and Discovery evidence before interpreting them.