Can AI Agents Do Real Work? 5 Tasks to Test
Searches for AI agents real world use cases often lead to demos that skip the hard questions: did the agent finish the task, use the right source, avoid an unsafe action, and save enough time to justify its cost? A useful evaluation measures the whole workflow, including mistakes and the human review it still needs.
What counts as an AI agent?
An agent uses a model to decide among steps and tools while working toward a goal. A single prompt that returns text is not automatically an agent. The defining difference for your evaluation is whether the system can take actions through tools, inspect results, and continue or stop under defined rules.
Five tasks worth testing
- Source-based research: ask it to answer a narrow question using a fixed list of primary sources. Score whether every factual claim has a supporting citation.
- Data cleanup: provide a synthetic CSV with known duplicates, blanks, and inconsistent dates. Compare the output against a prepared answer key.
- Small code change: request a contained fix in a test repository. Run the same tests before and after, and inspect the diff for unrelated edits.
- Calendar planning: have it propose a schedule from constraints, but require approval before creating, moving, or deleting any real event.
- Support triage: classify fictional messages into a small schema. Measure label accuracy and whether ambiguous cases are escalated.
Use one scorecard for every run
Record the date, model and version, tools enabled, prompt, input, expected outcome, actual outcome, elapsed time, token or usage cost if available, corrections made, and whether a human had to intervene. Repeat each task several times: one impressive run is not reliable evidence.
Score correctness against an answer key, not confidence or fluency. Track unsafe actions separately. Keep approvals for external writes, payments, account changes, and messages. OpenAI’s guidance recommends layered safeguards, tool approvals, and evaluation; safeguards reduce risk but do not make an agent error-proof.
Decide whether it is useful
Compare agent-assisted completion with the same task done manually. Include setup and review time. A task is a good candidate when the result is verifiable, errors are reversible, and the remaining human work is smaller than before. Keep a person in control when mistakes can affect money, safety, privacy, or a customer’s rights.
Try it: copy the scorecard into a spreadsheet, choose one synthetic task, and run five trials before letting an agent touch live data.
Next step: Use a practical data engineering roadmap to build the skills behind reliable automation →
Reference: OpenAI guide to agent safety. Product behavior and model versions change; rerun your own test before relying on results.