Claude Code Walkthrough: A Bug to Shipped Fix

Abstract claims about agentic coding are easy to make and hard to trust. So instead of another list of capabilities, here's a concrete walkthrough: one realistic bug, traced through a large unfamiliar codebase with Claude Code, from the first vague report to a merged and verified fix. The specifics are illustrative, but the shape is exactly how these sessions actually unfold — including the dead ends, because pretending agentic work is a straight line helps no one.

The scenario: a payments service in a 700k-line monorepo intermittently double-charges a small fraction of customers. The on-call engineer has never worked in this service. The report is one sentence and a customer ID.

Step one: orient in an unfamiliar service

The first job isn't to fix anything — it's to understand where the relevant code even lives. This is where a large context window earns its keep. You point Claude Code at the report and ask it to map the charge flow: which handler receives a payment request, where idempotency is supposed to be enforced, and where the actual charge call to the provider happens. The agent reads across dozens of files and returns a narrated map of the flow with the specific function names and the line where the provider is called.

This orientation step would take a human new to the service most of a day. The value isn't that the agent is smarter — it's that it can hold the whole charge path in context at once and explain it back, so you start the investigation with a mental model instead of grep results.

Step two: form and test hypotheses

With the flow mapped, you ask the obvious question: where could the same charge fire twice? The agent proposes three hypotheses — a retry without idempotency, a race between two workers consuming the same queue message, and a webhook that re-triggers the charge. Rather than guessing, you have it gather evidence for each: find the retry logic, check whether the idempotency key covers the retry path, and inspect the queue consumer for at-least-once delivery handling.

flowchart TD
  A["Vague bug report"] --> B["Claude maps charge flow"]
  B --> C["Generate 3 hypotheses"]
  C --> D["Gather evidence per hypothesis"]
  D --> E{"Idempotency key covers retry path?"}
  E -->|No| F["Root cause: retry skips key"]
  E -->|Yes| G["Check queue & webhook paths"]
  F --> H["Write failing test reproducing double charge"]
  H --> I["Implement fix, run suite"]
  I --> J["Review semantic diff, merge"]

The evidence points clearly: the idempotency key is set on the initial request path but the retry helper constructs a fresh request without it, so a timeout-triggered retry charges again. The race and webhook hypotheses don't hold up under the evidence. Crucially, you didn't take the agent's first guess as truth — you made it show its work, which is the discipline that separates a real fix from a plausible-sounding one.

Step three: reproduce before you fix

A fix you can't reproduce is a fix you can't trust. So before changing anything, you have the agent write a failing test that simulates a timeout and asserts exactly one charge reaches the provider. This matters for two reasons. First, it proves the root cause is real rather than a coincidence. Second, it becomes the verification gate: when the test goes green, you have objective evidence the bug is dead, not just a vibe.

The agent writes the test, you confirm it fails for the right reason — two charge calls instead of one — and now there's a target. This reproduce-first step is the single biggest difference between agentic work that ships safely and agentic work that ships regressions.

Step four: implement the smallest correct fix

Now the actual change, and here you keep the agent on a short leash. The fix is to thread the idempotency key through the retry helper so retries carry the original key. You explicitly tell the agent: change only the retry path, do not refactor the surrounding code, keep the diff minimal. This is deliberate scope control — the temptation for an agent (and a human) to "clean up while we're here" is exactly how a one-line fix becomes a risky forty-file diff.

The agent produces a tight change: the retry helper now accepts and forwards the idempotency key. The previously failing test goes green. The rest of the suite stays green. You've got a candidate fix with objective evidence behind it.

Step five: review the diff like an adversary

Green tests are necessary, not sufficient. You review the diff with suspicion, and the first thing you check is whether the agent touched any tests — it didn't, good. Then you read the implementation for silent semantic changes: did it alter any default, change a timeout, or broaden a catch block? It didn't. Finally you sanity-check the fix against the other two hypotheses you set aside, confirming they were genuinely ruled out and not just deprioritized. This adversarial review is where you catch the failures that automated checks miss, and it takes minutes because the diff is small by design.

Step six: ship and capture the lesson

The change merges through the normal pipeline — the agent never touched a protected branch directly. But the work isn't quite done. The reason this bug existed is that the retry helper was an easy place to forget the idempotency key, and nothing prevented it. So you add a short note to the service's CLAUDE.md: any new request-constructing helper must carry the idempotency key, and there's now a test enforcing it. That single note means the next time the agent works in this service, it starts with the knowledge that prevented this class of bug. The codebase got a little more agent-proof, which is the quiet compounding benefit of doing this well.

Frequently asked questions

How does Claude Code help in a codebase the engineer doesn't know?

Its large context window lets it read across many files and narrate how a flow actually works — which handler, which idempotency check, which external call — so an unfamiliar engineer starts with a mental model instead of raw search results. That orientation step is often the most time-consuming part of debugging an unknown service, and it's where the agent saves the most time.

Why write a failing test before fixing the bug?

A reproducing test proves the root cause is real and becomes the objective gate that confirms the fix worked. Without it, you're trusting that a change made the symptom disappear, which is exactly how regressions ship. Reproduce-first is the discipline that makes agentic fixes trustworthy.

How do you stop the agent from over-fixing?

State scope explicitly — change only this path, don't refactor adjacent code, keep the diff minimal — and treat an unexpectedly large diff as a red flag. Scope control turns a one-line fix into a one-line fix instead of a sprawling, risky change you have to review in full.

What makes the review step worth the time?

Automated checks confirm the code runs and tests pass; adversarial review catches what they can't — weakened test assertions, silent default changes, and root-cause hypotheses that were dropped rather than ruled out. Because a well-scoped fix is small, this review takes minutes and catches the failures that would otherwise reach production.

Bringing agentic AI to your phone lines

The same loop — orient, hypothesize, verify, ship — drives agentic systems beyond code. CallSphere runs it on your voice and chat channels, with multi-agent assistants that answer every call, pull data mid-conversation, and complete real work 24/7. See it live at callsphere.ai.

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Source & attribution: This is an independent, original explainer inspired by Anthropic's coverage on the Claude blog. Claude, Claude Code, Claude Cowork, Claude Opus, and the Model Context Protocol are products and trademarks of Anthropic. CallSphere is not affiliated with or endorsed by Anthropic.