GPT-5.4 Computer Use Practical Guide: The first playbook in an era where AI operates your computer for you

Problem Definition

GPT-5.4 was released on March 5, 2026, the Computer Use function, which allows AI to directly manipulate the user's computer, was installed for the first time. This article provides step-by-step guidance from setup to actual automation scenarios so that practitioners can immediately apply GPT-5.4 Computer Use to their work.

Applies to:

• Practitioners who want to automate repetitive desktop tasks (file organizing, data entry, report generation)
• Teams that are already using or considering adopting Codex or ChatGPT Pro
• Users who want to delegate multi-step workflows of 10 or more steps to AI

Not applicable:

• Security-sensitive systems (financial transactions, direct manipulation of medical records) require separate approval and audit systems
• Offline environment or API inaccessible environment

Evidence and comparison: GPT-5.4 vs previous models vs competitors

Judgment criteria:

• long context + long output + autonomous execution then GPT-5.4
• If only simple inference is needed, GPT-5.2 is sufficient
• Claude's computer use stability is still at beta level

Step-by-step execution method

Step 1: Check access rights and select model

On ChatGPT:

Settings > Select model > GPT-5.4 Thinking

• Plus/Team/Pro subscription required
• Pro/Enterprise can select GPT-5.4 Pro (maximum performance)

In Codex CLI:

/model gpt-5.4

#Set inference effort (xhigh recommended for complex tasks)
#Options: xhigh, high, medium (default), low, none

Step 2: Enable Computer Use

Image input settings (when using API):

{
  "model": "gpt-5.4",
  "messages": [...],
  "image_detail": "original"  //Up to 10.24M pixels, best click accuracy
  //or "high" (2.56M pixels, fast response)
}

Computer Use Authorization (CUA) approval:

• Display permission request pop-up on first use
• Select “Allow computer control for this session”
• Automatically release at end of session (Security)

Step 3: Toggle speed mode (Codex)

#Fast mode (1.5-2x faster response, 2x faster token usage)
/fast

#return to normal mode
/fast off

When to use:

• Debugging, simple code generation: /fast Recommended
• Complex multi-step automation: maintain normal mode

Step 4: Create Practical Automation Prompts

Effective prompt structure:

[Role]: You are an automated agent that operates my desktop.
[Goal]: {Specific Outcome}
[step]:
1. {First task}
2. {Second task}
...
[Verification]: Check with screenshot after completing each step
[Restrictions]: {What not to do}

5 practical automation scenarios

Scenario 1: Slack + Jira + Report Automation

1. Collect today’s messages from the Slack #dev-updates channel
2. Extract the mentioned Jira ticket number (PROJ-XXX)
3. Check the status of each ticket in Jira
4. Organize the results into a markdown table
5. Save as status-report.md file

Estimated time: 3-5 minutes (replaces 30 minutes+ manual effort)

Scenario 2: Web data collection → Spreadsheet

1. Open [URL] page
2. Extract table data
3. Create a new Google Sheets sheet
4. Paste data
5. Auto-adjust column width

Scenario 3: Code refactoring + test execution

/model gpt-5.4
Analyze the entire src/utils folder of your project:
1. Identify duplicate functions
2. Extraction with common utilities
3. Automatically modify existing import path
4. Run npm test to check if it passes

Scenario 4: Create a batch of email drafts

Read the contacts.csv file and for each contact:
1. Draft a personalized email with your name and company name
2. Save it as {name}.txt in the drafts/ folder.
3. Print out the list of files created after completion

Scenario 5: Automate local file organization

Clean out your Downloads folder:
1. Create subfolders by extension (pdf, docx, png, etc.)
2. Move files
3. Files older than 30 days are stored in the Archive folder.
4. Summary output of cleaning results

Pitfalls

1. Granting too much permission

Problem: Allow full system access to Computer Use

Prevention: Grant only per-session permissions, block access to sensitive folders (~/.ssh, financial apps)

Recovery: Immediately end session, check changed files with git diff

2. Inference effort misset

Problem: Set none or low for complex tasks → Missing steps, incorrect clicks

Prevention: Multi-step work is medium or more, more than 10 steps is xhigh

Recovery: Abort operation and retry with xhigh

3. Insufficient image resolution

Problem: low Screen input → Misrecognition of UI elements

Prevention: When using a computer, original or high required

Repair: Increase resolution and rerun same prompt

4. Skip verification step

Problem: Just believing the “completed” response and not checking → In reality, it fails

Prevention: Specify “Attach screenshot after completing each step” in the prompt

Recovery: Check result manually, rerun from failed step

5. Speed ​​mode overuse

Problem: Complex automation in /fast state → Degraded accuracy

Prevention: Simple coding only /fast, automation in normal mode

Recovery: /fast off and then retry

Execution Checklist

• Check GPT-5.4 access rights (Plus/Team/Pro/Enterprise)
• Approve Computer Use Authorization pop-up
• Set image input resolution (original or high)
• Determine the level of inference effort (medium basic, if complex, xhigh)
• Sensitive folder/app access blocking settings
• Try first in a test environment
• Includes verification prompt for each step
• Prepare rollback plan in case of failure

Completion criteria: If the automation workflow succeeds three times in a row in a test environment and the output of each step matches expectations, it can be applied to production.

Reference material

1. OpenAI GPT-5.4 official announcement - 2026-03-05
2. TechCrunch: OpenAI launches GPT-5.4 - 2026-03-05
3. OpenAI Community: GPT-5.4 Deep Dive - 2026-03-05
4. GoSearch: GPT-5.4 for Agentic Workflows - 2026-03-05
5. Fortune: OpenAI GPT-5.4 Enterprise - 2026-03-05

Author's perspective

Recommended:

• Practitioners who perform repetitive desktop tasks of 10 or more steps every day are recommended to adopt immediately
• If you are a Codex user, you can feel productivity improvement just by switching to /model gpt-5.4
• In particular, the combination of code refactoring + test automation has the highest ROI

Not Recommended/Caution:

• Prohibited to be used in security-sensitive work (financial, medical, legal) without a separate approval system
• The mindset of “AI will take care of it” is risky → Verification step is required
• It is still in beta, so back up important data before working on it

If another choice is better:

• Only simple text generation required: GPT-5.2 Thinking is sufficient
• No-code automation: No-code tools like Zapier/Make are more reliable
• Enterprise audit log required: Claude Enterprise provides more detailed logging