A reviewable handoff for untrusted agent data
Agent workflows increasingly consume datasets collected outside the team’s direct control. That can be useful, but it changes the delivery question from "did the run finish?" to "what evidence can a reviewer inspect before the result reaches another system?"
A practical handoff has four small stages:
- Scan inputs for prompt-injection indicators or unexpected instructions.
- Quarantine records that need a person’s decision, instead of silently treating them as ordinary data.
- Review the remaining input alongside source references and warnings.
- Compare the approved snapshot with the next run so structural and semantic drift is visible.
This is intentionally not a claim that a scanner makes an untrusted dataset safe, or that a diff is a quality certificate. The point is narrower: preserve enough context for an operator to challenge the result and decide what happens next.
For teams building these bounded checks into an Apify workflow, these are separate components so they can be evaluated independently:
Find them here:
- Prompt Injection Dataset Scanner v2: https://apify.com/zentrafoundry/prompt-injection-dataset-scanner-v2
- Prompt Injection Quarantine: https://apify.com/zentrafoundry/zentra-prompt-injection-quarantine
- Dataset Diff Engine v2: https://apify.com/zentrafoundry/dataset-diff-engine-v2
The useful outcome is not autonomous trust. It is a reviewable record of what entered the workflow, what was flagged, and how the data changed over time.
Top comments (0)