Claude Code Plan Mode: When to Use It and How to Review the Plan

Claude Code Plan Mode: When to Use It and How to Review the Plan

Claude Code plan mode is worth using when a task touches several files, involves a part of the codebase you do not know well, or has more than one plausible approach. In plan mode, the agent reads the project and proposes a plan without changing anything. Edits start only after you approve. The gain comes from catching a misunderstanding in the plan, where fixing it costs one sentence, instead of in code that has already been changed.

Key takeaways

• Plan mode separates two phases: exploring the code (read-only) and editing (only after the plan is approved).
• It pays off on tasks that affect several files, unfamiliar code, migrations and refactors. On small, obvious fixes, it only adds a step.
• A good plan lists the files involved, the order of changes, the risks, what stays untouched and how to verify the result.
• Reviewing the plan is work for someone who knows the business, not only someone who knows the syntax. Approving without reading cancels the benefit.
• Plan mode lowers the risk of premature edits, but it does not replace restricted permissions, review of the final code and testing.

What plan mode is and what it changes in practice

Claude Code works with permission modes. In the default mode, it asks for your authorization before actions such as editing files or running commands. In plan mode, the agent is limited to investigating: it reads files, searches for code, learns the structure and talks through the approach with you. When it finishes, it presents a plan and asks for your decision: approve, adjust or keep planning.

According to the official documentation, you can switch modes during a session with the Shift+Tab shortcut, which cycles through the permission modes, or start a session already in plan mode using the command-line option for the permission mode. The exact option names and the behavior of the approval screen change between versions, so check the documentation for the version your team uses before you standardize an internal procedure.

What changes in practice is the order of the work. Without plan mode, the agent tends to read a little, decide and start editing. With it, the decision becomes a readable artifact, a plan, that you can correct before there is anything to undo.

Why separating exploring from editing reduces rework

Most rework on AI-assisted coding tasks comes from a wrong understanding of the problem, not from typos. The agent thought the discount rule lived in one module when it lived in another. It assumed it could change an API contract without warning the people who consume it. It picked a library the project avoids for a historical reason.

When exploring and editing happen together, that mistake only shows up in the result: a set of scattered changes you have to read, understand and revert. When they are separate, the mistake shows up in the plan, in a line like change the calculation function in this file, and you fix it with one sentence.

There is also an effect on context. Exploration uses up conversation space, and the plan works as a summary of what matters. This ties into what we explain in what a context window is in AI: the more clutter accumulates, the worse the later decisions get. A short, reviewed plan gives the agent a clean starting point for the editing phase.

A note of honesty: this comes from implementation experience and process reasoning, not from a documented guarantee. Plan mode does not make the model smarter. It only creates a checkpoint before the change.

Which tasks are worth using plan mode for

A simple decision guide, as a list:

• Use it when the change affects several files or layers (interface, business rules, database).
• Use it when you or your team do not know the relevant part of the code well.
• Use it for migrations, dependency updates and refactors, where the order of the steps matters.
• Use it when there are at least two plausible approaches and the choice has consequences (for example, changing an API contract or creating a new table).
• Use it when the request is ambiguous: the agent can ask questions before assuming.
• Use it to investigate a problem before fixing it, as in the workflow described in how to debug production bugs with AI, where hypotheses come before any fix.

And when not to use it:

• Small, obvious fixes, such as correcting a piece of text, renaming a local variable or changing a color.
• Tasks you have already described precisely, naming the file and the expected change.
• Throwaway experiments on an isolated branch, where reverting is trivial.

The practical rule: the more expensive a change is to undo, the more the plan pays off. For a company that maintains systems tied to customer support, billing or customer data, almost every non-trivial change falls into the first list.

How to run a planning session, step by step

A routine that works for most tasks:

• Step 1: enter plan mode before you ask for the task. Confirm in the interface that the mode is active.
• Step 2: describe the business goal, not just the technical change. Say who uses this feature, what must not break and what is out of scope.
• Step 3: point to where to look, such as files, folders or decision documents. If the project has conventions, ask the agent to read them first.
• Step 4: ask for questions before the plan. If the agent asks nothing on an ambiguous task, be suspicious.
• Step 5: ask for the plan in a reviewable format: affected files, order, risks, what will not be changed and how to verify.
• Step 6: review, question and request adjustments without leaving plan mode. Repeat until the plan is clear.
• Step 7: only then allow execution. Decide whether edits will be approved one at a time or in a batch, depending on the risk of the task.

If your team repeats this routine often, turn it into a standing instruction. This is exactly the kind of procedure that fits in Claude Code skills, so everyone asks for plans in the same format.

How to review the plan before approving changes

Approving a plan without reading it is just a slower way of editing blind. The review does not need to be long, but it needs to be done by someone who understands what the system does for the business. A review checklist:

• Scope: does the plan solve the requested problem and only that? Are there extra changes nobody asked for?
• Files: do the listed files make sense? Is anything missing that you know will be affected, such as tests, documentation or configuration?
• Order: does the sequence let you stop midway without leaving the system broken?
• Data: does any step read, write or delete customer data? If so, is it clear which data and why? In projects that handle personal data, this question connects to LGPD obligations and your internal policy.
• Dependencies and access: does the plan ask to install new packages, call external services or use credentials? Each item needs a justification. When integrations run through MCP in Claude Code, check which scope they are configured in and what each tool can do.
• Stated risks: does the plan admit uncertainty? A plan with no risks listed is usually shallow.
• Verification: is there a concrete way to know it worked, such as tests to run or behavior to check by hand? On trusting tests that the AI itself writes, see AI-generated tests: can you trust them.
• Rollback: if it goes wrong, how do you go back? Working on a separate version control branch usually solves this.

If any item is weak, do not approve. Ask the agent to fix it, still inside plan mode. Useful prompts are: show me where you saw this in the code, what alternative did you discard and why, and what happens to existing behavior if this step fails.

Illustrative scenario: changing the rounding rule on a quote

Imagine a small company that maintains an internal quoting system. Someone asks: round the total up to fixed multiples. It looks like one line of code.

In plan mode, the agent reads the project and returns a plan. It finds that the total calculation appears in two places: on the quote screen and in the PDF generated for the customer. It also notes that a monthly report sums the saved totals and that the change would alter comparisons with earlier months. And it asks whether the rounding applies only to new quotes or also to quotes already issued.

Without the plan, the edit would probably change only the screen, leaving the PDF with a different value from what the salesperson saw. With the plan, the team decides that only new quotes are affected, adds the PDF to the scope and asks for a test that compares screen and document. The difference is not that the agent got it right the first time, but that the mismatch surfaced before any code existed.

This is an illustrative scenario, built to show the reasoning. It does not describe any real project or measured result.

Limits of plan mode

Three limits worth making clear to your team:

• A convincing plan can be wrong. The text is fluent even when the premise is false, so verify the claims about the code before approving.
• Plan mode is a process control, not a complete security boundary. Permissions, isolated environments and minimal access to credentials are still necessary. If the agent reads files containing third-party content, the risk of prompt injection also remains.
• The plan does not replace reviewing the result. After execution, read the changes and run the tests as you would with anyone's work.

There is also the time cost. Planning everything, including the trivial, creates friction, and the team ends up approving by reflex. Reserve the mode for where it changes the decision.

How this fits a working method

Plan mode is a small application of the same stages we use when introducing AI into business processes: map, limit, pilot and hand over. Mapping is the exploration phase, where the agent understands the code and you check that understanding. Limiting is the approved plan: scope, files, access and what stays out. Piloting is running one real task, with a record of what was asked, planned and delivered, and a success criterion agreed beforehand. Handing over is documenting the procedure so the team can repeat it without depending on whoever created it, and then deciding whether to expand, adjust or stop.

As a reference for practice, Anthropic's material on building agents and on context engineering covers closely related ideas: breaking work into verifiable steps and keeping in context only what helps the decision.

Frequently asked questions

Does Claude Code plan mode prevent any changes? During the planning phase, the agent is limited to investigating and proposing, without editing files. Even so, do not treat it as a substitute for well-configured permissions and a separate branch for the work. The exact behavior can vary between versions, so confirm it in the documentation.

Do I need to use plan mode on every task? No. For small, well-described fixes, it only adds a step. Use it when the change crosses several files, involves unfamiliar code or has more than one possible path.

Who should review the plan? Someone who understands the impact of the change on the business and on the system, not necessarily the person who requested the task. For changes involving customer data, it is worth including whoever is responsible for data protection in your company. The point is that approval should be a conscious decision, not a click.

What if the plan comes back vague or with wrong assumptions? Do not approve it. Stay in plan mode and ask the agent to show where it saw each piece of information in the code, list the alternatives it discarded and state the risks. If the plan is still weak, shrink the scope of the task and start with a smaller part.

Sources

• Anthropic: Claude Code, best practices for agentic coding
• Anthropic: Building effective agents
• Anthropic: Effective context engineering for AI agents