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When to Use Agent vs Ask Mode

You need to understand how your payment processing module works before refactoring it. You open the AI in its default agent mode, ask “how does payment processing work in this codebase?” and the AI immediately starts reading files, running grep commands, and consuming 40,000 tokens of context before giving you a three-sentence answer. You could have gotten the same answer with a fraction of the context usage if you had used the right mode.

Every tool — Cursor, Claude Code, and Codex — provides modes that control how much autonomy the AI has. The spectrum ranges from “read and analyze only” to “execute everything autonomously.” Understanding this spectrum and when to use each setting is fundamental to working efficiently.

  • A clear mapping of modes across all three tools
  • Decision criteria for choosing the right mode for each task type
  • Prompts optimized for each mode
  • Strategies for transitioning between modes during a single workflow

All three tools share the same fundamental spectrum, even though they use different terminology:

CapabilityCursorClaude CodeCodex
Read-only analysisAsk mode (or Plan mode to draft a plan)Plan Mode (Shift+Tab)--ask-for-approval untrusted + --sandbox read-only
Guided executionAgent mode (default)Normal Mode (default)--ask-for-approval on-request (or the --full-auto preset)
Full autonomyAuto-Run “Run Everything” / Cloud Agent--dangerously-skip-permissions / Sandbox--ask-for-approval never

Use this when you want to understand code without modifying it. The AI reads files and answers questions but cannot make changes.

Switch to Ask mode in the mode selector. The AI will analyze code and answer questions without making changes. For investigations that should end in a written plan rather than edits, Plan mode is the sibling to reach for:

How does the authentication flow work? Trace the request from
the login endpoint through middleware to the session store.
Show me the key files and functions involved.

Ask mode is token-efficient because the AI focuses on answering your question rather than exploring broadly. It uses the codebase index for retrieval rather than reading files one by one.

Best for: Code review, architecture analysis, onboarding to a new codebase, understanding unfamiliar code, investigating bugs before fixing them.

The default mode for all three tools. The AI can read files, make changes, and run commands, but asks for permission at key points. This is the workhorse mode for most development tasks.

Agent mode is the default. Cursor’s agent reads files, proposes edits, and runs terminal commands. You can review changes in the diff view before accepting:

Implement the rate limiter middleware following the pattern in
@src/middleware/auth.ts. Write tests in @src/middleware/__tests__/.
Run the tests after implementation.

Configure which tools the agent can use in Cursor Settings. You can allow file edits but require approval for terminal commands, or vice versa.

Best for: Feature implementation, bug fixes, refactoring, test writing, most day-to-day development work.

The AI runs without interruption. Powerful for well-defined, low-risk tasks. Dangerous for anything touching sensitive code.

Auto-Run set to Run Everything (informally called YOLO mode) auto-accepts all changes and commands. Cloud Agent runs tasks asynchronously in an isolated cloud environment:

Fix all ESLint warnings in src/components/. Run npm run lint
after each fix to verify. Commit each fix separately with a
descriptive message.

Cloud Agent is the safer option for autonomous work. It runs on a clone of your repo in an isolated cloud environment, so your local working directory stays untouched until you review and merge the changes.

Best for: Lint fixes, formatting, bulk renames, documentation generation, test boilerplate, migration scripts across many files.

Use this flowchart when choosing a mode:

  1. Are you trying to understand code, not change it? Use read-only/analysis mode.
  2. Is the task well-defined with clear verification? Use guided execution mode and let the AI work through the task with your periodic review.
  3. Is the task mechanical and low-risk? Consider full autonomy mode with appropriate isolation (cloud agent, sandbox, cloud thread).
  4. Is the task touching sensitive code? Use guided execution with per-file approval.
  5. Are you unsure what mode to use? Start with guided execution. You can always loosen permissions mid-session.

Advanced teams use different modes simultaneously across multiple sessions:

Run a Cloud Agent on a lint-fix task (autonomous) while you use the main Agent for feature development (guided). Review the Cloud Agent’s changes when they are ready, without interrupting your feature work.

You stay in analysis mode too long. If you spend 30 minutes asking the AI about the codebase before writing any code, you have consumed context that could have been used for implementation. Set a time box for analysis (5-10 minutes), then switch to implementation.

You use full autonomy on complex tasks. Autonomous mode works for mechanical tasks with clear verification. For tasks requiring judgment (API design, error handling strategy, performance optimization), guided mode with human review produces better results.

You mix modes within a single prompt. Asking the AI to “analyze the auth module, then refactor it” in a single prompt forces it to switch modes internally, which often leads to it skipping the analysis and jumping straight to refactoring. Separate analysis and implementation into distinct prompts.

You forget to switch back. After using analysis mode for investigation, some developers forget to switch back to execution mode and wonder why the AI is not making changes. Check your current mode if the AI seems unresponsive to implementation requests.