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To keep an AI agent from taking a desktop or file task further than you intended, make it propose a precise finish line before it changes anything. Ask it to state the result, the exact deliverable, a user-checkable completion test, work that is explicitly out of scope, and questions that could change the plan. Then require it to stop and wait for your approval. After approval, it should stop when the agreed test passes and report unrelated observations without acting on them.
This “define done” workflow creates a pause for clarification; it is not a safety guarantee. Ben Patterson described the approach in PCWorld on August 13, 2026, and reported a successful test with dummy files. That is an author’s demonstration, not a controlled study or proof that the prompt prevents harmful changes in every agent or application.
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What is a “define done” prompt?
A define-done prompt asks an agent to describe exactly what completion means before it starts work. Instead of treating “organize this folder” or “rename these screenshots” as permission to make every change it considers useful, the agent must first propose a bounded result and obtain your go-ahead.
The method separates planning from execution. In the planning reply, the agent identifies the smallest complete version of the request, explains how you will verify it, lists actions that are not included, and raises unresolved questions. It must then pause. Only after you approve that proposal may it act.
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The approach is useful for agents that can edit files, rename items, move documents, or operate desktop applications. It does not make an agent infallible, and it cannot compensate for granting excessive permissions or approving an unclear plan.
How does the prompt stop an agent from going beyond the request?
It inserts an explicit decision point before side effects occur. The agent has to turn a broad goal into a finite contract that you can inspect.
- Interpretation: The agent states the intended result in concrete terms.
- Scope: It names the files, settings, or other deliverables it will change and identifies related work it will leave alone.
- Verification: It defines a check you can perform, such as a folder structure, naming pattern, count, or test result.
- Clarification: It asks questions whose answers could alter the finish line.
- Approval: It stops instead of beginning the requested work in the same reply.
- Execution: After approval, it performs only the agreed work and stops when the agreed check passes.
The original wording includes this direct instruction: “Then stop. Do not begin work in the same reply — wait for my go-ahead.” That sentence appears in the prompt reproduced by Ben Patterson in PCWorld.
What should the prompt include?
The fuller version uses five headings. Each answers a different question a user should settle before an agent touches data.
Result
Describe the outcome, not a vague activity. “Create a date-based archive of the project’s final invoices” is more bounded than “clean up the invoices.” Tell the agent to choose the smallest complete version of the request so it does not expand a simple job into a general improvement project.
What you’ll produce
List the specific deliverable or change: a set of renamed files, a new folder structure, a report, or a defined configuration. Include limits such as which directory is included, which file types may be moved, and whether content may be edited or only names and locations.
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How I’ll know
Give the user a checkable completion test. A useful test might be “all screenshots in the selected folder use the YYYY-MM-DD-description format and no files outside that folder were changed.” If the result cannot be verified, the agent has no reliable stopping point.
Not part of done
State tempting adjacent work that must remain untouched. Examples include deleting duplicates, changing file contents, modifying permissions, searching other drives, or reorganizing sensitive records. This section is the boundary against well-intentioned extras.
Open questions
Ask only questions that could change the definition of done. If a folder contains financial, tax, legal, insurance, or identity records, the agent should ask whether those records belong in a separate category rather than silently deciding.
What is a copy-ready full prompt?
Adapt the bracketed details to your task. The important behavior is the order: proposal first, approval second, execution third.
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You are helping with a bounded task. Before doing any work, propose the finish line using these headings:
Result
State the smallest complete outcome for my request. Avoid vague goals.
What you’ll produce
List the exact files, folders, settings, or other deliverables you would create or change. Limit the work to [location, project, or item].
How I’ll know
Give me a concrete check I can perform to confirm completion, including any count, naming rule, test, or before-and-after condition.
Not part of done
List related actions you will not take, such as deleting, editing contents, changing permissions, searching outside the stated location, or reorganizing sensitive material unless I approve it.
Open questions
Ask any question whose answer could change the result, deliverables, verification method, or exclusions.
Then stop. Do not begin work in the same reply — wait for my go-ahead.
After I approve, perform only the agreed work. Stop once the agreed completion check passes. Report other observations separately instead of acting on them.
Replace broad phrases with measurable boundaries. “Organize my files” needs a location, file categories, permitted operations, and a verification rule. If you cannot fill in a field, let the agent identify it as an open question rather than guessing.
How do I use it for a desktop or file task?
Use the prompt as a two-message workflow. Do not combine the planning request with an instruction to start immediately.
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- Name the target: Identify the exact folder, application, account, or project. Say whether subfolders are included.
- Describe allowed changes: Specify whether the agent may move, rename, create, or edit items. State what it may not delete or alter.
- Request the proposal: Paste the full prompt and your task details. Wait for the five-part response.
- Inspect the boundaries: Check that the proposed deliverables match your intent, the verification test is observable, and exclusions cover risky adjacent work.
- Answer open questions: Resolve ambiguities, such as whether sensitive records should be separated or whether duplicate files may be touched.
- Approve explicitly: Reply with a clear go-ahead only after the proposal is correct. If anything is wrong, ask for a revised proposal instead.
- Verify the result: When the agent reports completion, perform the agreed check yourself and review its separate observations before authorizing any new task.
For example, an instruction to organize a folder might limit the agent to moving and renaming files in that folder, forbid deletion and content edits, and require a final list of every changed path. If the agent notices a second folder that appears related, it should mention it separately rather than include it automatically.
What did the reported test show?
Patterson said he tried the prompt on a directory containing a few dozen dummy files and folders. He asked ChatGPT to organize it. The agent asked whether sensitive financial, tax, legal, insurance, and identity records should be placed in a separate category; Patterson answered, and the agent then requested a final go-ahead. He reported that the files were organized in about a minute.
He also reported refining the wording with GPT 5.6 Sol and Claude Opus 5. These details show how the prompt was developed and used by one author. They do not establish that every agent will ask the same questions, that the organization was optimal, or that the method reduces risk by a measured amount.
What are the limits of the evidence?
The PCWorld account is an author-reported demonstration with dummy data. It was not an independent evaluation, a statistically powered experiment, or a comparison of products. No published numerical efficacy finding shows how often the prompt prevents unwanted changes.
An agent can still misunderstand a boundary, misclassify a file, apply an unsafe tool action after approval, or stop after a check that was itself poorly designed. The approval gate also depends on you reading the proposal carefully; clicking “yes” to an ambiguous plan removes much of the protection. Use backups, version history, or a disposable copy when a task could cause irreversible loss.
How should I judge a full prompt against a compact version?
A compact version can be convenient for routine, low-impact work, while the five-heading version exposes more assumptions for review. Neither format has measured superiority in the available account. Compare them by the safeguards they make visible.
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| Criterion | Full five-heading prompt | Compact prompt |
|---|---|---|
| Finish-line completeness | Separates result, deliverables, verification, exclusions, and questions. | Combines those checks into one shorter instruction; details depend on how much context you add. |
| Out-of-scope clarity | Provides a dedicated “Not part of done” section. | Requires you to state exclusions in the same sentence or paragraph. |
| Approval pause | Contains an explicit instruction to stop and wait before acting. | Should retain the same explicit stop-and-wait language. |
| User-checkable test | Requires a separate “How I’ll know” criterion. | Must include a concise completion check or the agent may infer one. |
| Open questions | Lists ambiguities before execution. | Can ask for questions, but they are easier to overlook in a short paragraph. |
Use the fuller form when files are sensitive, the operation is broad, or reversal is difficult. A compact form is reasonable only when the target, allowed actions, exclusions, and check are already unambiguous.
Where is this workflow most useful?
File organization and renaming
Moving files and changing names can create widespread confusion even when no data is deleted. Define the exact directory, naming convention, categories, duplicate policy, and a report of changed paths. Explicitly exclude content edits and deletion unless they are truly required.
Desktop automation
For an agent operating menus or applications, define which application and window it may use, which settings it may change, and what screen state proves completion. Prohibit unrelated settings changes and require it to stop if a confirmation dialog or unexpected account appears.
Research and document preparation
Specify the sources or files to examine, the output format, and whether the agent may add information beyond those materials. A completion test might require every requested section to be present and every unresolved issue to be listed rather than silently filled with assumptions.
What should I do when the agent’s proposal is unsafe or vague?
Do not approve it. Ask for a narrower revision and identify the missing boundary.
- It proposes deletion: Change the deliverable to a report or a reversible move, and require a separate approval for deletion.
- It searches beyond the target: Name the allowed path and state that other drives, accounts, or folders are out of scope.
- The check is subjective: Replace “looks organized” with a naming rule, count, manifest, or test result.
- It ignores an open question: Ask the question directly and require the proposal to be regenerated before execution.
- It adds useful extras: Keep them in the separate-observations report; create a new define-done proposal if you want them performed.
- It starts immediately: Stop the run if possible, inspect what changed, restore from a backup or version history, and repeat with the approval instruction placed at the end of the planning request.
Can the prompt guarantee that an AI agent will stay within scope?
No. It is a communication and review pattern, not a permission system, sandbox, or rollback mechanism. Technical controls such as least-privilege accounts, restricted folders, backups, and human confirmation for destructive actions remain necessary for high-impact work.
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The prompt is most valuable when it makes hidden assumptions visible before execution. Treat the proposed finish line as a draft contract that you must inspect, not as evidence that the agent is safe by default.
How do I adapt the wording for different agents?
The structure is portable across software agents, including ChatGPT, Claude, and Gemini. Preserve the behavioral requirements rather than relying on a particular model’s default style: define the five fields, require a pause, and state that unrelated observations must not trigger action.
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Some agents may present a confirmation button, while others require a typed reply. Use an unambiguous approval phrase and do not assume that a conversational acknowledgment equals permission to modify files. If an agent cannot reliably wait, perform the planning step in a read-only session and execute only after you have separately confirmed the exact commands or changes.
What is the practical takeaway?
Ask the agent to define done before it does anything. A precise result, deliverable list, user-checkable test, exclusion list, and open-question review create a deliberate pause that broad requests lack. The method can reduce accidental scope expansion, but its success still depends on clear instructions, careful approval, and technical safeguards around the agent.
Frequently Asked Questions
Can I use a define-done prompt for an irreversible task?
You can use it to clarify the plan, but do not treat the prompt as authorization for irreversible action. Require a separate confirmation immediately before deletion, publication, payment, or any other change that cannot be easily undone, and keep a recoverable copy when possible.
What if I do not know the right completion test yet?
Tell the agent that the verification method is an open question and ask it to suggest alternatives without acting. Choose a test you can independently observe, such as a manifest, exact count, naming pattern, or successful dry run.
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Limit the target to the smallest necessary location and state how confidential items should be treated in the proposal. Review the service’s account, storage, and access settings separately; the prompt itself does not change those privacy controls.
How can I tell whether an agent actually stopped at the agreed boundary?
Compare its completion report with the stated deliverables and inspect the target for unexpected changes. Ask for a list of changed paths, timestamps, or commands when the tool can provide them, then check that list against your approved scope.
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