On this page#
What changes when an agent does this#
Upgrading Claude Code is not mainly about installing a newer version or writing a smarter one-shot prompt. The useful upgrade is a workflow change: let the agent produce a reviewable patch, then make a human verify intent, invariants, and evidence before that patch becomes trusted code.
Claude Code can inspect a repository, edit files, and run development tools, as described in the official Claude Code overview. Once an agent can perform those actions, typing speed is no longer the bottleneck. Review quality is.
The work moves through four states:
- Repository understanding: What does the code appear to do?
- Change planning: What should change, and what must remain unchanged?
- Patch production: Which files, interfaces, and tests are affected?
- Acceptance: Does the patch satisfy the real contract of the repository?
That last state cannot be delegated wholesale. A passing test suite proves only that the executed tests passed. It does not prove that the agent understood an undocumented invariant, preserved an operational constraint, or avoided expanding the task.
A code review is a structured examination of source-code changes before acceptance; the basic purpose and common forms are summarized in the code review reference. With an agent, review must begin before the diff exists. If the plan is wrong, a polished patch merely makes the wrong change harder to notice.
| Weak workflow | Reviewable workflow | |---|---| | “Fix this bug” | Define the failure, constraints, and acceptance evidence | | Agent edits immediately | Agent inspects first and proposes a bounded plan | | Tests are the verdict | Tests are one input to a human gate | | Large final diff | Small, explained change set | | Follow-up repair by guesswork | Rollback to a known checkpoint |
This is the real answer to how to upgrade Claude Code for production work: upgrade the control loop around the agent.
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The prompt chain#
Use separate prompts for inspection, planning, implementation, and review. Each stage should produce an artifact that can be rejected without contaminating the next stage. A single giant prompt hides assumptions; a prompt chain exposes them while correction is still cheap.
The Claude Code documentation explains the product’s capabilities and operation. It does not remove your responsibility to define repository-specific invariants. Put those directly into the chain instead of assuming the agent will infer them.
Stage 1: Inspect without editing#
The first prompt should forbid changes and ask for evidence. This prevents the agent from converting its first plausible interpretation into code before it has mapped the relevant execution path.
Inspect the repository for this task. Do not edit files or propose a fix yet.
Task:
[Describe the observed problem.]
Identify:
- the entry points involved;
- the data and control flow;
- existing tests covering the behavior;
- repository rules or interfaces that constrain a fix;
- assumptions you cannot verify from the repository.
For every conclusion, name the file or symbol that supports it.Why this works: it separates facts from guesses. If the response cannot tie a claim to a file, symbol, test, or explicit task constraint, treat that claim as unverified.
Stage 2: Plan around invariants#
The second prompt should demand the smallest coherent change and force the agent to state what it will preserve. “Minimal” does not mean fewest lines; it means the narrowest change that completely satisfies the contract.
Propose a change plan. Do not edit files.
The following invariants must remain true:
- [Invariant 1]
- [Invariant 2]
- [Invariant 3]
For each planned edit, explain:
- why the file must change;
- which behavior changes;
- which behavior must not change;
- how the result will be verified;
- the clean rollback boundary.
List rejected alternatives and why they are worse.
Stop if the task conflicts with an invariant.The rejection list matters because agents are good at making one path sound inevitable. Requiring alternatives exposes whether the chosen design follows the repository or merely fits the prompt.
For a reusable version of this staged process, PairFoundry’s Review and Repair pack focuses on turning agent output into inspectable work rather than treating generation as completion.
Stage 3: Implement within the approved boundary#
Implementation should be constrained by the accepted plan. The agent must stop when reality contradicts that plan instead of silently redesigning the task.
Implement only the approved plan.
Rules:
- do not modify unrelated files;
- do not change public interfaces unless the plan explicitly requires it;
- preserve the listed invariants;
- add or update tests for the changed behavior;
- stop and report if repository evidence contradicts the plan.
After editing, report:
1. files changed;
2. behavior changed;
3. verification performed;
4. remaining uncertainty;
5. exact rollback steps.This prompt makes scope drift visible. The final report is not proof, but it gives the reviewer a checklist to challenge against the actual diff.
Stage 4: Attack the patch#
The final agent pass should behave like a skeptical reviewer, not the author defending its work. Ask it to search for counterexamples and missing evidence.
Review the patch as if you intend to reject it.
Check for:
- violated invariants;
- behavior changes outside the task;
- missing error paths and boundary cases;
- tests that prove implementation details but not outcomes;
- stale callers, documentation, or configuration;
- claims unsupported by the diff or verification output.
Separate blocking findings from optional improvements.
Do not edit anything.If this review produces new blockers, return to planning. Do not let the agent quietly patch its own review findings; that collapses the gate back into an unchecked loop. Developers who need the underlying workflow concepts before adopting a packaged process can use PairFoundry’s free foundations track.
The gate#
The gate is a deliberate human stop between the agent’s review and acceptance. The reviewer must inspect the approved plan, the actual diff, and the verification evidence together. If any one of them disagrees with the others, the change does not pass.
At the gate, answer these questions in order:
- Is the intended behavior correct? Confirm the task itself, not the agent’s paraphrase.
- Did the patch stay inside scope? Every changed file needs a task-related reason.
- Are the invariants preserved? Trace important callers, data boundaries, and failure paths.
- Do tests exercise outcomes? A test that mirrors the implementation can pass while the contract remains broken.
- Is uncertainty explicit? “No known issues” is not evidence.
- Can this be reverted cleanly? Identify the exact patch or commit boundary before acceptance.
Human review is not ceremonial approval after automation. It is the point where business intent and repository evidence meet—concerns that ordinary code review already addresses, but agent-generated changes make easier to skip.
Reject the patch when its explanation is clearer than its evidence. Eloquence is not traceability.
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What it gets wrong#
Claude Code can produce locally convincing changes that are globally wrong. The recurring failures are not usually syntax errors; they are false assumptions, widened scope, shallow tests, and repairs that preserve the symptom while violating the repository’s contract.
Watch for these patterns:
- Invented invariants: The agent presents a convention as mandatory without locating its source. Ask for the enforcing code, test, configuration, or instruction.
- Scope laundering: An unrelated refactor is described as “required cleanup.” Remove it unless the task cannot be completed without it.
- Happy-path repair: The visible case works, but empty, malformed, delayed, or partial inputs remain wrong.
- Test-shaped implementation: The patch satisfies one asserted example rather than the behavior the example represents.
- Caller blindness: A changed function works alone while existing callers still rely on the old semantics.
- Configuration omission: Code changes, but defaults, schemas, generated artifacts, or operational instructions remain stale.
- Self-review convergence: The same context that created the mistake reviews it and repeats the same assumption.
The official Claude Code overview describes an agentic coding tool, not an oracle for your repository’s unwritten rules. Detect these failures by asking for disconfirming evidence: “What repository fact would make this patch wrong?” A useful review attempts to falsify the change.
More agent workflows are organized in PairFoundry’s workflow library, while the packs overview shows the available packaged paths.
Rollback#
Rollback should restore one known repository state without asking the agent to improvise another repair. Create a clean checkpoint before implementation, keep the task isolated, and revert the complete change set when an invariant fails. Partial manual undo is where residue survives.
Use this sequence:
- Record the pre-change repository state.
- Keep the agent’s edits in one isolated change set.
- Save verification output separately from source changes.
- If the gate fails, discard or revert the entire change set.
- Return to the inspection stage with the failed assumption documented.
- Generate a new plan; do not stack a speculative repair on the rejected patch.
Rollback is not failure. It is evidence that the gate worked.
Avoid asking, “Undo whatever caused the problem.” That invites another interpretation pass. Name the exact boundary to restore, then confirm that no generated files, configuration edits, or test fixtures remain. The operational details of Claude Code belong in the official documentation; the rollback boundary belongs to your repository workflow.
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Related reading#
- Cursor review: making the output reviewable, not just correct
- Devin review — the workflow, the gate, and the rollback
FAQ#
Can I upgrade my Claude code subscription?#
Yes, subscription changes are an account or plan-management task, but that is separate from upgrading the quality of Claude Code’s output. A higher allowance may enable more usage; it does not make patches reviewable. For production repositories, adopt the inspection, planning, implementation, gate, and rollback chain above.
Does Claude Code upgrade itself?#
Product-update behavior depends on how Claude Code is installed and operated, so use the current official Claude Code documentation for the applicable procedure. Do not confuse automatic software updates with workflow improvement: a newer client does not supply your repository’s invariants or replace human acceptance.
How do I force the Claude code to update?#
Follow the update instructions for your installation method in the official documentation. Do not invent commands from memory or copy an instruction intended for a different installation. If “update” means improving output, enforce staged prompts and a human gate rather than forcing another unconstrained generation pass.
How do I increase my Claude code limit?#
Limits and plan options are service concerns covered by Anthropic’s official materials. Increasing a limit can provide more agent capacity, but capacity is not control. First reduce wasted loops by separating inspection from editing, rejecting unsupported assumptions early, and rolling failed changes back to a clean boundary.