Criteria for Delegating Tasks to AI: Three Axes for Deciding What to Ask AI to Do
About This Article
When I first started building a site with Vibe Coding, I often struggled with the question of how much to delegate to AI. Delegating too much left me unable to handle problems when they arose. Being too cautious meant the work did not move forward.
This article introduces three decision axes I developed through actual work. I have written them as criteria that can be applied without technical knowledge.
This article evaluates an individual task before delegation. I cover the human role during and after implementation in The Human Role When AI Writes Code.
Axis 1: Reversibility (Can It Be Undone?)
The first question to ask is: “If AI makes a mistake, can I return to the state before the change?”
Reversible → easier to delegate
Changes like updating button color, rewriting a paragraph’s text, or adding a new section are easy to undo. When Git is used, the change history remains, and the change can be reverted if something goes wrong.
Difficult to reverse → verify first
Operations like rewriting the contents of a database, changing a published URL, or sending an email to an external recipient are difficult to undo after the fact. Before delegating these to AI, it is necessary to review the content and impact of the change yourself.
Axis 2: Scope of Impact (Where Does the Change Reach?)
The second question is: “How far does this change reach?”
Localized change → easier to delegate
A change like “increase the font size of this heading on this page” affects only one location. Even if there is a problem, it can be caught by checking that one place.
Wide-reaching change → proceed with care
Changes like “restructure the navigation across the entire site,” “modify the authentication system,” or “change styles that are shared across all pages” affect many parts of the site when one location is changed. After AI makes the change, preparation is needed to be able to verify where the effects have appeared.
The wider the scope of impact, the more useful it is to confirm with the AI what will be changed and how before actually making the change.
Axis 3: Ease of Verification (How Can the Result Be Confirmed?)
The third question is: “How do I verify what AI has done?”
Verifiable by appearance or behavior → easier to delegate
Design changes, text editing, and link placement can be verified by looking at the display in a browser. No specialized knowledge is needed.
Requires specialized knowledge to verify → human must confirm
Security settings, performance optimizations, and connection configurations for external services are cases where correctness is difficult to judge from the display alone. For tasks like these, it is necessary either to understand the verification method before delegating or to proceed with someone who can verify the result.
Example Decisions Using the Three Axes
“Change the button color on the top page from blue to white”
- Reversibility: high (can be reverted with Git)
- Scope of impact: narrow (one button on one page)
- Ease of verification: high (visible in browser) → Easy to delegate to AI
“Modify the user authentication system”
- Reversibility: low (problems after the change can be complex to address)
- Scope of impact: wide (affects login, registration, and session management)
- Ease of verification: low (security issues cannot be confirmed from the display) → Proceed carefully with a solid understanding of what is being changed
Summary
Using the three axes together makes the “delegate or not” judgment more concrete.
| Axis | Easier to Delegate | Proceed with Care |
|---|---|---|
| Reversibility | Can be undone | Difficult to undo |
| Scope of Impact | Localized | Wide-reaching |
| Ease of Verification | Visible in browser | Requires specialized knowledge |
When all three fall in the “easier to delegate” column, the task can be confidently handed to AI. If any one of them falls in the “proceed with care” column, I recommend addressing that axis before moving forward.