Describe the behaviour instead of trusting the label
For this guide, an agent is software that uses a model to choose the steps towards a task, and may call tools to fetch information or take an action. Vendors use the word loosely, so ask for the actual sequence of inputs, decisions, tools and outputs. The label alone tells you nothing about autonomy, reliability or recruitment capability.
A fixed workflow follows defined rules, such as creating a reminder after a recorded event. An AI-assisted draft may involve one model response and no external action. An agent may choose several intermediate steps. These are useful working distinctions for evaluation, not a universal product taxonomy.
Follow a bounded research task
Take a hypothetical task: prepare a company brief from approved public sources before a BD call. The agent searches, reads the pages and drafts a summary with citations. A consultant checks the sources and decides whether the brief is any use. The task ends there unless another action is separately authorised.
Ask what happens when sources conflict, a page is unavailable or the company name is ambiguous. A useful result can say the evidence is unresolved. It should not invent a contact, assume a vacancy exists or send a message simply to make the task look complete.
Separate tool access from permission to act
List the tools the agent can call and the accounts they use. Read access to a record doesn’t imply authority to change it. A connection that can send messages shouldn’t give the model unlimited discretion over recipients and wording. When you compare AI recruiting agents, ask to see exactly where that boundary sits.
The Model Context Protocol specifies a way for applications to exchange context and expose capabilities. Using it doesn’t prove that your application’s permission checks, approvals or business rules are correct. Ask the implementer to show those controls working under ordinary user access.
Keep candidate decisions with accountable reviewers
For a summary of candidate evidence, insist on references back to the original application and a visible way of flagging uncertainty. The recruiter checks the material before using it. An agent’s ranking is not proof of qualification, availability or likely job performance.
Use difficult cases in the trial: an incomplete date, an unfamiliar qualification, a career change and conflicting notes. Record whether the agent preserves the evidence and whether the reviewer can correct it. A successful research demo does not validate a different task such as shortlisting.
Define failure and recovery
Give the task a stopping condition and a way to hand control back to a person. Check what happens after a tool error, expired access or a half-finished run. If an external action may already have happened, check the destination system before retrying.
In a hypothetical record-update workflow, distinguish a proposed change, human approval, a queued operation and confirmation from the destination system. The interface should not call a pending operation complete. Keep an operator responsible for conflicts and unresolved failures.
Buy a verified workflow, not a promise of autonomy
Ask the supplier to run the exact task on authorised test material, not a prepared demo. Record the configuration, sources, permissions, outputs, corrections and total reviewer effort. Confirm which capabilities are available in your account and which are still proposals or roadmap items.
Approve a narrow use with explicit limits, then reassess before expanding it. The question that matters is whether the complete workflow is useful and controlled for your team. Neither an agent count nor a general model benchmark answers that on its own.
Sources and reading
Primary sources checked 9 October 2026. The practical evaluation steps in this article are editorial recommendations, not measured customer outcomes.
