Claude Code review: using Anthropic’s terminal agent and staying focused during long runs

2026-04-27 · 5 min read · ZenCode

Claude Code is different from every other AI coding tool covered in this series. It does not live in your IDE. It does not complete your next line. It sits in a terminal window and takes over your codebase for minutes at a time — reading files, writing code, running tests, fixing the failures, and reporting back when it thinks it is done.

That capability gap is also a focus gap. When Cursor autocompletes a function in 3 seconds, the attention cost is low. When Claude Code runs a 12-minute refactor across 40 files, the question “what do I do while it works?” is not a minor UX issue. It determines whether you end the task with a codebase you understand or one you accepted.

This review covers what Claude Code does well, where the focus model breaks down, and the specific habits that keep you in review mode rather than passive observation mode during a long run.

What Claude Code actually is

Claude Code is a terminal-based agentic coding assistant from Anthropic, the maker of the Claude model family. You install it via npm install -g @anthropic-ai/claude-code, run it in a project directory, and give it a task in plain English. It then reads your files, plans an approach, writes code, executes shell commands, runs your test suite, and iterates until the task is done or it gets stuck.

The key difference from IDE-based assistants: Claude Code has actual tool use. It calls read_file, write_file, bash, and search_files as real operations, not simulations. When it says “running tests” it is actually running your tests. When it says “I found 3 failing assertions” those are real failures in the output it read from your CI.

This is genuinely more capable than inline autocomplete, but it introduces a class of review problems that inline tools do not have: you are reviewing a completed body of work, not a suggested continuation. The cognitive overhead of reviewing 40 changed files is qualitatively different from reviewing one suggested completion.

The focus problem is structural, not willpower

Inline autocomplete creates a reflex problem: the Tab key fires faster than your review reflex. Claude Code creates a different problem: long runs create a waiting state, and waiting states invite distraction. The run takes 8 minutes. Your brain decides that is enough time to check Slack, read a thread on X, or switch to a different task. When Claude Code finishes, you return to review a diff that is larger than you expected, covering files you forgot were in scope, with the momentum of “it looks done, should I just accept it?”

That momentum is the real focus risk with Claude Code. Not the Tab reflex that inline tools create, but the “it ran for 12 minutes, surely it figured it out” authority transfer. The length of the run becomes a proxy for correctness. It is not.

What Claude Code does well

Before getting into the review habits, it is worth being specific about what Claude Code actually does well, because it is genuinely impressive in certain classes of tasks.

Refactoring with test coverage: If your project has good test coverage, Claude Code can execute large refactors safely. It reads the tests, makes the change, runs the tests, fixes failures, and iterates. For tasks like “rename this field across the codebase and update all the tests” or “extract this logic into a shared utility and update all callers,” it performs at a level that would take a human 90 minutes to do carefully.

Exploration and explanation: Claude Code can traverse a large codebase and tell you what it does in a way that is grounded in the actual files. The explanations are not hallucinated generalizations — they are derived from the specific file contents it read. For onboarding to a new codebase or understanding a legacy system, this is a genuinely useful capability.

Debugging with visible reasoning: When you give Claude Code a failing test or a stack trace, it shows you the steps it takes to trace the failure: which file it read, what it found, which hypothesis it formed. This reasoning trace is useful for your own understanding, not just for the fix.

Dependency and migration tasks: Upgrading a library, migrating from one API to another, converting a codebase from JavaScript to TypeScript — these are high-volume mechanical tasks where Claude Code’s ability to touch many files systematically is genuinely useful.

Where it creates focus debt

The risks are not in the mechanical execution. Claude Code is reliable at finding and editing files. The risks are in judgment calls it makes silently while you are waiting.

Scope creep in agentic tasks: Claude Code frequently does more than you asked. You say “fix the failing test in auth.ts” and it also refactors the adjacent helper, renames a variable it found confusing, and adds a comment explaining something it noticed. Each individual change may be reasonable. The cumulative effect is a diff you did not fully specify and need to review from scratch.

Silent assumption changes: When the model encounters ambiguity — should this function return null or throw? should this field be optional? — it makes a decision and moves on. It does not pause to ask. You find out in the diff. If the decision was wrong, and it sometimes is, the error is embedded in working code that passes tests.

Invented utilities and abstractions: Long agentic runs sometimes produce utility functions, shared helpers, or base classes that did not exist before and that you did not ask for. The model decided they were useful. Some of them are. Others are speculative abstractions that add complexity without value. They appear at the end of the diff, where you are most likely to skim.

Four habits for staying in review mode during a long Claude Code run

1. Read the plan before approving it. Claude Code shows a planned approach before it starts executing. This is not a formality. Read it. The plan reveals scope: which files it plans to touch, what approach it will take, what it will not do. If the scope is larger than you expected or the approach is different from what you had in mind, now is the time to redirect — not after 10 minutes of execution that touched the wrong files.

2. Set a mental checkpoint before the run starts. Before Claude Code begins, decide the one thing you will do during the wait that is directly related to this task: read the tests it will need to pass, sketch the approach you expected, or just sit with the terminal and watch the output scroll. This is not about being productive — it is about staying in the context so the diff review does not require you to re-enter it from zero.

3. Start the diff review from the last file, not the first. The model had the most context at the start of the run. The first file is where it was most careful. The last file is where scope creep appears: the speculative utility it added, the extra refactor it decided was helpful, the comment explaining a decision you did not ask for. Read the last file first. If something unexpected is there, you can reject the whole run before spending time reviewing the earlier (correct) work.

4. Reject the authority-transfer instinct explicitly. When the run finishes and the diff is large and the tests pass, the default response is “it figured it out.” That is the authority transfer. Replace it with a concrete question: “What is one thing this diff does that I did not specifically ask for?” If you can name it, you are reviewing. If you cannot, you are accepting. For large agentic runs, the answer to that question is almost always something.

Claude Code versus other agentic tools

The focus problems above are not unique to Claude Code. Cline has the same silent-assumption problem. GitHub Copilot Workspace creates the same momentum-of-completion effect. What Claude Code adds that is specific to its design: the terminal context means your IDE is free. That is convenient but it also means you have a clean, low-friction path to opening Slack or a browser while it runs. IDE-based agents stay in your visual field. Claude Code disappears into a terminal tab.

If you use Claude Code regularly, the structural fix is to keep a second terminal tab with a simple loop that posts a desktop notification when the run finishes — so you can stay on a separate task with a defined handoff point rather than watching the terminal anxiously. That is better for focus than watching streaming output, and it gives you a clean moment to shift back into review mode when the notification fires.

The honest verdict

Claude Code is the most capable AI coding tool available for tasks that require reading and writing many files with actual execution of real commands. For the right tasks — large refactors with test coverage, dependency migrations, codebase exploration — it is not close to any other tool in the space.

The focus cost is real and specific: long runs create authority transfer, large diffs create review fatigue, and the terminal context removes the visual reminder that something is running. The four habits above address those specific failure modes. Used with those habits, Claude Code is the most productive single change you can make to a coding workflow that already involves AI tools.