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Before assigning an AI-generated task list—or letting an agent act on it—treat it as a draft. Rewrite each item so it names a result, defines what is in and out of scope, identifies missing information, and explains how someone will verify completion. Then remove genuine overlap and set review and approval limits proportionate to the risk.
What a usable task needs
Australian Government design guidance recommends identifying goals and success criteria, breaking larger goals into manageable tasks, assigning responsibility, ordering work logically, defining dependencies, and avoiding duplicated responsibility. Those principles provide a practical way to repair an AI-generated list; they are not a prescribed universal template.
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- Outcome: What result should exist when the task is done?
- Scope and constraints: What is included, excluded, or limited?
- Owner: Who is responsible for the next action?
- Inputs: What facts or materials are required, and are they available?
- Dependencies: What must happen first, and what work follows?
- Completion evidence: What can a person inspect to verify the result?
- Authority: If an agent will act, what may it do, and what requires escalation?
Use this as a practical checklist, not as an empirically validated scoring instrument. The level of detail should fit the task and the consequences of error.
Repair the task list before assigning it
1. Rewrite vague activities as outcomes
Replace broad verbs such as “research,” “review,” or “improve” with a result and a way to recognize it. For example, change “Research competitors” to “Compare the three named competitors’ publicly listed prices and core features in a table, and flag any information that could not be verified.” The revised task identifies a deliverable and a check without pretending that missing facts are known.
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If the intended result cannot be inferred reliably, ask the person who requested the work. A task list should not conceal uncertainty behind confident-sounding instructions.
2. Add scope, constraints, inputs, and dependencies
State the relevant boundaries and materials: for example, which products, documents, time period, or systems are in scope. Specify prerequisites so the sequence is clear. If the deadline, owner, required input, or acceptance criterion is unknown, record it as an open question and get it from the responsible person or source rather than letting the AI fill in a plausible guess.
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Google Search Central says AI-generated material should be manually fact-checked and reviewed for accuracy and trustworthiness before publication. Applying that principle to task lists is a practical extension: check the factual assumptions in a task before acting on them.
3. Find and resolve overlap
Compare entries by the outcome they seek, the deliverable they produce, the owner, and the responsibility assigned. If two tasks request the same result or give the same responsibility twice, combine them or make their distinct purposes explicit. Keep subtasks when they produce separate outputs or represent real dependencies; similar wording alone does not prove that two tasks are duplicates.
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There is no objective duplicate-detection rule established here. Use the work’s intended outputs and ownership to make the distinction, and ask the requestor when the difference is unclear.
4. Define completion evidence
For each task, specify what evidence will show it is finished: a reviewed document, an approved decision, or a verified change are examples. The check should fit the outcome. Do not treat any one acceptance-criteria format as mandatory; the cited guidance supports defining success criteria but does not prescribe a single template.
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5. Choose the level of human review
Microsoft recommends weighing repeatability, the impact of an error, how readily an error can be detected, and time sensitivity when deciding whether work should be automated, AI-supported, or human-led. Use these questions to decide how much oversight a task needs:
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- Repeatability: Is the work routine and consistent, or does it require judgment in a new situation?
- Impact: What would happen if the result were wrong or incomplete?
- Detectability: Can someone readily check and correct an error before it causes harm?
- Time sensitivity: Does the work need a rapid response, or is there time for review?
High impact or difficult-to-verify results are reasons to keep a person in the lead or require meaningful human review. Do not assume that an AI-produced completion claim is itself evidence that the work is correct.
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6. Set boundaries before an agent acts
A list used only to organize human work is different from instructions given to an agent that can take actions. The UK National Cyber Security Centre warns that “The way a task is described can have a significant impact on the agent’s behaviour.” Give an acting agent a bounded objective, explicitly permitted actions, prohibited actions, and conditions for stopping and escalating to a person. Keep visibility into what it does and retain human accountability for deployment, access, safeguards, and consequences.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Decide who should lead the work
Repeatability, impact, error detectability, and time sensitivity help distinguish human-led work from AI support or automation with review. When an agent can take actions, also consider whether its scope is clear and whether a reliable stop-and-escalate control exists. These are decision factors, not a formula that assigns every task to one category.
Quick Recap
| Work approach | When it may fit | What to check |
|---|---|---|
| Human-led | Impact is high, the task calls for substantial judgment, or errors are hard to detect before they matter. | Keep responsibility with a person and define the evidence needed to accept the result. |
| AI-supported | AI can help with a bounded part of the work, while a person can review its output. | Make the scope and inputs clear, check the output, and resolve uncertainties before relying on it. |
| Automation with human review | The work is repeatable and errors can be detected and corrected through an appropriate review. | Set review points that match the impact and time sensitivity, and ensure someone can intervene. |
| Agent with action authority | An agent is permitted to do more than draft or suggest work. | Define allowed and prohibited actions, stop conditions, escalation, and meaningful human oversight. |
Run a final check before using the list
- Read each task and confirm that its outcome is identifiable.
- Check that scope, inputs, owner, constraints, and dependencies are explicit—or marked as questions to resolve.
- Compare outcomes and responsibility across tasks; combine true overlap and clarify legitimate separate work.
- Write down the evidence that will show each result is complete.
- Set review effort according to repeatability, impact, error detectability, and time sensitivity.
- Before granting an agent authority, define its permitted actions and when it must stop for human approval.
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