What makes AI work reviewable?

Approval is one point in a longer process

An approval button answers whether a person authorizes an action. It does not establish that the source data was complete, that the proposed change solves the intended problem, or that the action eventually succeeded. Designing reviewable AI work means making those questions answerable separately.

Proto records the steps of its work and can prepare documents, investigate records, and act through connected tools. The value of that trace depends on the task definition and the reviewer’s ability to connect a result to its evidence. A long activity history alone does not guarantee a sound decision.

Four objects to keep distinct

Consider an illustrative task: preparing reminders for overdue invoices. The process has at least four separate objects:

1. The request: which accounts, which reporting date, and which action is intended.
2. The evidence: invoice records, relevant correspondence, and unresolved data conflicts.
3. The proposed action: the exact recipient, message, or record change presented for review.
4. The observed result: what the destination system reports after execution.

These objects should agree, but they are not interchangeable. An invoice balance does not establish that a reminder is appropriate. A drafted reminder is not a sent message. A successful tool response may still need inspection in the destination when the business consequence matters.

Put the evidence near the decision

A reviewer should not need to reconstruct an entire session to understand one proposed action. Ask for a compact evidence bundle: the affected record, supporting source, material uncertainty, and expected consequence. Where a calculation affects the decision, preserve inputs and formulas rather than only the final number.

For bulk work, ask for the proposed set before execution. The shape of the set matters as much as an individual item: excluded accounts, duplicate identities, and empty or missing values can reveal a faulty selection rule. Sample ordinary cases alongside outliers. A review of only the most striking examples can miss a systematic error.

Configure the action boundary deliberately

New Proto installs start in Uninterrupted mode, where confirmable actions run without stopping for approval. With it off and outside Plan mode, supported consequential browser actions, including sending, paying, and deleting, request approval. Bulk ERP•AI updates also request review while sensitive actions are disabled. ERP•AI deletion and access changes have separate settings and remain disabled until enabled.

That is a product control, not a promise that every possible error will be prevented. Give a bounded brief and review the approval controls before starting. If the desired output is a draft, say so explicitly. The task instruction describes the work you want; permission settings govern relevant actions along the way.

Close the loop with observable evidence

After an action, inspect the result at the level of its consequence. For a generated document, open the artifact and check the material figures and formatting. For a record change, inspect the intended fields and affected records. For a message, confirm the destination and status through the application used to send it.

This yields a practical distinction between prepared, approved, executed, and verified work. Keeping those states explicit makes handovers more reliable and avoids reporting a plan or draft as a completed business action.

Evaluate the process, not only the answer

When piloting an agent workflow, test cases with missing data, conflicting sources, duplicate identifiers, and a changed requirement. Observe whether the process surfaces uncertainty and whether the reviewer can stop or correct the work at a useful point. A reviewable process gives people enough context to exercise judgment, then preserves enough evidence to understand what happened afterward.