Start with an action catalogue
List the actions in the proposed recruitment workflow: search, read, summarise, draft, save, schedule and send. Name the user or role allowed to authorise each action. That list is more useful than a broad statement that an agent will manage recruitment from end to end.
For a Sydney agency evaluating a new tool, begin with one desk and one task, such as summarising the notes on one consultant’s live vacancies. Confirm the records involved and the system of record. Do not assume the permissions for public company research are appropriate for private candidate notes or client communications.
Separate preparation from commitment
An agent can be evaluated for preparing work while a person keeps the decision to act. A draft shortlist, a proposed record correction and a ready-to-send message carry different consequences, so give each a visible review state and the evidence the reviewer needs.
Our guide to AI recruitment software in Australia draws the same line stage by stage, with what AI prepares in one column and what the recruiter decides in the next. Test the split with a hypothetical case. A consultant asks for a follow-up plan for active vacancies, and the agent prepares suggestions from approved records. It does not contact candidates or change application stages. The consultant checks whether the sources are current and accepts only the relevant suggestions.
Make approval specific
Approval should identify the content, destination, affected records and intended action. If the agent changes any of those material details after approval, return the proposal for review. Avoid a blanket approval that can be stretched to cover a different recipient or new information.
Test whether a rejected proposal stays rejected and whether a scheduled action can be cancelled before it runs. Keep the audit record tied to the final version. A timestamp that says approved is no use if the team can’t establish what was approved.
Design for uncertain information
Ask the agent to keep its source references and to separate current facts from assumptions. If the original record is incomplete or contradictory, route the issue to a person. Do not allow the system to infer a candidate’s availability or qualification just to complete a workflow.
Use a hypothetical duplicate-record case to test the boundary. Two profiles may look like the same person but carry different contact details. The agent may prepare a comparison for review; merging or overwriting should require an authorised decision supported by the evidence.
Define recovery before adding more actions
For each external action, ask how the application establishes whether it happened. Keep the pending, failed and confirmed states distinct. Test expired access, a conflicting edit and a timeout after a request. The operator should know when a retry is safe and when reconciliation is required.
The Model Context Protocol can standardise connections between applications and capabilities, but it is not evidence that business approval rules are enforced. Inspect the actual permission and validation boundaries in the proposed implementation.
Expand only with a new decision
Review the pilot by task completion, evidence quality, corrections and operator effort. Report the cases tested and the problems still unresolved. Avoid claiming a placement increase or a time saving unless the data supports the exact statement.
If the team later proposes autonomous sending or record updates, treat that as a scope change with its own review. Keep a named person responsible for the workflow and its effects. A useful future for AI agents in recruitment depends on clear action boundaries and on keeping human judgement in consequential work.
Sources and reading
Primary sources checked 9 October 2026. The practical evaluation steps in this article are editorial recommendations, not measured customer outcomes.
