How to evaluate an agent before making the work recurring
A compelling demo answers whether a task can work once. Evaluation asks which conditions make its results dependable enough to use.
Translate the objective into observable behavior
Start with the business result and the evidence that would demonstrate it. “Prepare an accurate project brief” is incomplete. Specify required sections, authoritative sources, freshness requirements, and how unresolved facts should appear. The reviewer should be able to say which requirement passed or failed without relying on whether the writing sounds confident. That makes the evaluation useful for both business and technical decisions.
For an illustrative project brief, acceptance might require the current milestone date, source links for every changed commitment, and a separate list of missing owners. The workflow should avoid presenting a proposed date as an approved one. These conditions derive from the job the brief serves. They are more informative than a generic score for helpfulness that hides the reason a result cannot be used.
Build cases from the real failure surface
Choose representative, approved inputs from the process you intend to support. Include ordinary variation in document format, wording, source completeness, and business status. Keep sensitive information within the approved testing environment. If synthetic examples are needed, label them clearly and keep them separate from production records. A fabricated customer should never appear as an actual business event.
Then add cases designed to expose mistakes: two conflicting dates, an outdated source, a missing attachment, an account with insufficient access, and a request beyond the permitted action. These cases should test the workflow's response to uncertainty. A correct outcome can be a visible escalation or refusal to proceed. Penalizing every pause encourages the wrong behavior in a process that depends on human decisions.
Evaluate the artifact and the action path
Read the output against its sources. Check whether citations resolve, facts are attributed correctly, and the result contains unsupported conclusions. Also inspect which tools and accounts were used. A correct-looking answer produced through an unauthorized source is not an acceptable workflow outcome. Record content quality and authorization behavior as separate findings so a formatting improvement cannot obscure an access problem.
For workflows that write to systems, test in a controlled environment with suitable safeguards. Check target identifiers, duplicate handling, and the result of a partial failure. The important question is whether an operator can explain what changed and recover safely. Avoid live customer sends or production record edits merely to prove that a button works. Those require their own authorized acceptance procedure.
Count the human work
Measure review effort and corrections as well as agent runtime. Record whether the reviewer needed to reopen every source, rewrite sections, or resolve repeated ambiguities. Compare with the prior process using similar cases. A fast draft may still increase total work if the output is difficult to verify. A slower draft can be useful if it organizes evidence clearly and surfaces the right decisions.
Keep the sample size and conditions visible when reporting results. Do not turn a handful of pilot runs into a universal accuracy or savings claim. Separate a repeated failure from an isolated ambiguity, and investigate its cause. Missing information might call for an intake change; wrong tool use might call for narrower permissions; inconsistent reasoning might require a different workflow design.
Test recurrence, interruption, and resumption
A one-time task may behave acceptably while repeated runs create duplicates or stale assumptions. Repeat cases across changed inputs and inspect what carries forward. In dots, context and saved notes can help continuity, but accepted business decisions should still live in the authoritative system. Verify that a corrected fact is reflected in later output and that an old assumption is not silently reintroduced.
Practice stopping and restarting. Dots has separate controls for the main task, delegated tasks, and schedules. Check what happens if a run stops after preparing an artifact but before its next action. The operator should know whether rerunning repeats work or continues from an accepted point. Record the recovery procedure with the workflow rather than leaving it in an individual's memory.
Create a release decision you can revisit
Keep the evaluated instruction, configuration, source set, results, and known limitations together. Name the person who accepts the workflow for its defined use. The decision should say what was tested, what remains untested, and what change will trigger another evaluation. A new app connection, new audience, changed prompt, or expanded write permission may alter the risk and behavior materially.
After acceptance, review exceptions from actual operation and add useful cases to the evaluation set. Maintain a small collection that represents the workflow's real responsibilities instead of a large suite of superficial checks. The objective is a repeatable way to decide whether a change improves the work and whether the system still respects the boundaries under which it was approved.
The source record
Sources & editorial notes
Primary documentation checked September 29, 2026. Our implementation recommendations are editorial analysis. Illustrative workflows are proposed examples, not completed client case studies or performance claims.
- Control your dot OpenAI
- Tasks and memory OpenAI
- Agents API overview OpenAI
- Agents SDK OpenAI
Found something that needs updating? Contact the editorial team with the passage and a supporting source.