Agnostics vs manual AI testing

Manual testing is useful. It is also easy to miss the inputs users will try when they are not following your script.

Manual AI testing

For: Small teams with tight release cycles and strong domain intuition.

Does well

• Explores nuanced product behavior humans care about.
• Cheap to start for one-off checks.
• Flexible for brand and tone review.

Breaks down

• Hard to replay the same hostile inputs consistently.
• Coverage drifts as the product changes.
• Findings live in Slack threads, not structured retest flows.

Structured adversarial scans

For: Teams shipping AI features who need reproducible failure signals.

Does well

• Repeatable attack packs and scan history.
• Findings grouped with severity and next moves.
• Release Gate and retest workflow built in.

Breaks down

• Requires target setup time.
• Not a substitute for human judgment on UX.
• Coverage depends on packs you choose.

What Agnostics adds

• Scan snapshots you can compare after fixes.
• Release Gate recommendations tied to policy.
• Retests that prove resolved means verified.

Honest limitations

• Agnostics does not replace product taste, legal review, or full red-team programs.
• It focuses on AI behavior failures your users can trigger.

When not to choose Agnostics

• You only need a one-time informal sanity check with no repeat releases.
• Your feature is not AI-driven.