The five-task AI agent trial
Agent demos are not enough. Before you adopt a tool, test it on five task types that reflect how your team actually works.
Recent agent research is a useful correction to tool hype. A task-stratified study of AI coding agents found that task type can matter more than the agent itself, with documentation, fixes, and new-feature work showing different acceptance patterns. Another head-to-head comparison of Claude Code and Codex on a scientific data-analysis pipeline showed why identical specifications and shared evaluation criteria matter. OpenAI’s Codex work with knowledge workers also reinforces that agents are spreading beyond pure software into reports, spreadsheets, analysis, and workflow automation.
The practical rule: compare agents on your work, not on their best demo.
The skill
A five-task AI agent trial is a small evaluation set for choosing or approving an AI tool. You pick five realistic tasks, define success criteria, run the same task brief through each candidate tool, and score the result before changing workflows.
Five-task AI agent trial
Agent or tool:
{name}
Task type:
{draft / fix / research / cleanup / automation / analysis}
Input:
{same input for each tool}
Expected output:
{what good looks like}
Success criteria:
{accuracy, completeness, usability, review effort, safety}
Failure modes:
{what would make the output unusable or risky}
Score:
{1-5 plus notes}The five tasks
Use five different task types. The exact set depends on your work, but this is a good default for knowledge teams:
- Draft: Create a first version of a memo, update, brief, or customer response.
- Cleanup: Normalize a messy spreadsheet, notes file, ticket list, or folder structure.
- Research: Gather source-grounded information and separate facts from assumptions.
- Fix: Improve an existing document, workflow, spreadsheet, script, or process.
- Automation: Build a small repeatable helper, checklist, report, or tiny tool.
A worked example
Suppose a small operations team is deciding whether to use an AI agent for weekly reporting.
Task 1: Draft
Create a weekly operations update from meeting notes.
Success: clear owners, decisions, risks, and unresolved questions.
Task 2: Cleanup
Normalize a CSV export with inconsistent region names.
Success: original row count preserved, changes explained, uncertain rows flagged.
Task 3: Research
Compare three vendor options from provided source links.
Success: every claim has a source, missing pricing is flagged.
Task 4: Fix
Improve an existing handoff checklist.
Success: fewer ambiguous steps, no invented process, review notes included.
Task 5: Automation
Create a small local template generator for recurring status reports.
Success: works on sample input, does not overwrite files, includes test instructions.The prompt
Use this to run one task through a candidate agent:
You are being evaluated for a five-task AI agent trial.
Task type:
{draft / cleanup / research / fix / automation}
Input:
{paste the exact same input for every agent}
Expected output:
{describe the required output}
Success criteria:
{list 3-6 criteria}
Constraints:
{source rules, privacy rules, files not to edit, actions requiring approval}
Before doing the task:
1. Restate the task.
2. Identify assumptions.
3. Name risks or missing context.
After doing the task:
1. Explain what you changed or produced.
2. List evidence or checks.
3. State what needs human review.How to score
Score every task on the same five criteria:
- Accuracy: Did it get the facts, calculations, or source details right?
- Completeness: Did it handle edge cases and required fields?
- Review effort: How much time did a human need to check or repair it?
- Workflow fit: Was the output shaped for the real next step?
- Safety: Did it respect boundaries, approvals, and source rules?
The rule
Do not choose an agent because it wins one impressive task. Choose it because it performs reliably across the task types your team repeats, and because its failures are easy to detect before they cause downstream work.