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A short code tour helps an AI coding agent find the right files because it narrows the question, names a few likely starting points, and makes the agent follow one concrete path through the code. That cuts down aimless searching across a repository. It does not make the agent’s answer correct. The map and explanation it produces are hypotheses that you check against the source and the tests.
Start with one behavior, not the whole repository
Asking an agent to “explain this codebase” gives it no finish line. It reads widely, summarizes what it finds, and gives you no clear way to tell whether the explanation covers what matters. A question about one behavior, such as where an API response is assembled or how a form saves its data, has a boundary. You can judge whether the answer traces that behavior from start to finish, and the agent has a reason to decide which files are relevant.
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Microsoft’s VS Code guidance on exploring a codebase with an agent takes this approach. It begins with a single behavior and, where you know it, the route, command, or UI element that triggers it.
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- Name the behavior. Describe one thing to understand, for example “where the order confirmation page’s data is loaded.”
- Give a starting point if you have one. A route, command, or UI element anchors the search and cuts down guesswork about where the path begins.
- Ask for a small map. Request the entry point, implementation modules, relevant configuration, and relevant tests, with a one-line explanation of each file’s role. The file-role table below shows what to look for.
- Have the agent follow one concrete input. Ask it to take a single request or input through the implementation and describe what goes in and what comes out, including error cases and external-service calls.
- Request references to files and symbols. Then open each referenced file yourself and confirm it is active code in the application you care about, and that the cited callers actually connect the steps the agent describes.
- Compare the explanation with the tests. Note which tests the agent read and which ones you actually ran. A test you only read has not passed.
- Keep two lists. Write verified facts in one place and assumptions and open questions in another. Only move something into shared documentation after someone has reviewed it.
The sequence follows the VS Code guide, which says an agent’s explanation should be checked against the code rather than treated as authoritative.
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What the map should contain and why each file belongs
A map is more useful when every entry comes with a reason. Asking the agent to justify each file is the quickest way to spot padding: a file it cannot connect to the behavior probably does not belong.
| Role in the map | What to ask the agent | What a good answer lets you check |
|---|---|---|
| Entry point | Which file first receives the request or triggers the behavior? | The entry point is reached from the starting route, command, or UI element you named. |
| Implementation modules | Which modules do the actual work, and in what order? | Each module is called from the step before it, not just named nearby. |
| Configuration | Which settings change this behavior, and where are they read? | The setting is read in a code path you traced, not only defined. |
| Tests | Which tests assert this behavior? | The test exercises the path in question and states what it expects. |
Trace one input through the code
The trace is where a tour earns its keep. Following a single request exposes the parts a summary tends to skip. When you review the agent’s trace, look for these points:
- Call sites: where each function is invoked, and whether the caller is live code.
- Values: what the data looks like at each step, including transformations.
- Return paths: how the result travels back to the caller, and whether any branch returns early.
- Error and external boundaries: where the code can fail, and where it hands work to a database, network service, or other system.
A trace that stops at a function name without showing its inputs and outputs has not yet answered the question.
When the references do not hold up
Treat the cited files as the first thing to check, not the last. These are the common failures and how to respond:
- The file exists but is not used. It may be dead code, a legacy copy, or a file excluded from the build. Search for live call sites before accepting it.
- The caller is named but not connected. The agent may have matched a similar name. Open the calling file and confirm the call happens on the path described.
- The explanation is complete but the tests disagree. Trust the test and the running code over the narrative. Re-run the relevant test before concluding anything.
- The map is much larger than the behavior. Narrow the question and ask again. A broad map usually signals a vague starting question.
Keep the repository map short and point to deeper documentation
OpenAI’s engineering account of working with Codex describes a short AGENTS.md file that acts as a table of contents. It points to a structured documentation directory. The idea is progressive disclosure: an agent starts from a small, stable entry point and follows pointers to deeper material only when a task needs it.
OpenAI reports the opposite pattern causing trouble. An oversized instruction file consumes scarce context, can hide constraints from the agent, and becomes hard to keep fresh and verify. These are lessons from one team’s experience, not a controlled comparison of documentation designs.
| Axis | Short map with pointers | One large instruction document |
|---|---|---|
| Scope | Tied to one task or behavior, with pointers for anything else | Tends to cover the whole repository at once |
| Structure | Compact entry point linking to deeper source material | Single monolithic file |
| Evidence | Source and symbol references you can open and check | Depends on the author; no evidence standard is stated in the sources |
| Maintenance | Pieces can be reviewed and updated individually | Harder to verify and keep current, according to OpenAI’s account |
| Context access | Loads only what a task needs | Consumes context whether or not it is relevant |
This comparison is an inference from the official workflow and OpenAI’s guidance. It is not a formal vendor benchmark.
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How you give an agent context matters as much as what you ask. GitHub’s Copilot documentation describes two ways to start: attach a repository to a chat, or ask from the repository page. It also supports directory, file, and symbol context, so you can narrow the agent’s view to the part of the code you are studying.
The documentation says questions asked in repository context work best when the semantic code search index is up to date. If answers seem to ignore recent changes, check the index before blaming the question. The repository-page flow is labeled as public preview and subject to change, so confirm its current status in GitHub’s documentation before building a process around it.
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What the evidence does and does not establish
A 2026 qualitative study, “How Developers Experience Debugging Unfamiliar Codebases with Code Tours Generated and Evaluated by Local LLMs,” reports how developers reacted to generated code tours. Participants generally preferred tours whose detail scaled with the length of the code, that were easy to scan, that avoided simply restating the code, and that used a guiding tone.
The same study found that developers trusted descriptions they believed were written by a person more than descriptions they believed were generated by AI. It also reports that LLM-generated annotations of tour quality were unreliable. That is a practical warning: do not use one model’s judgment to grade another model’s tour. These are findings about perception and presentation. They do not measure how much time a tour saves or how often it produces correct file lists, and no source in this area establishes a productivity figure.
Microsoft’s ShadowFrog project shows one file-backed approach. It keeps a shadow directory of Markdown organized by symbol, with references to source paths or file and symbol pairs. The project describes itself as experimental. It is a useful example of the category, but it is not independent proof that this kind of system produces better results.
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Starter questions for a bounded tour
GitHub’s Copilot guide offers example prompts that already have the right shape: a single behavior and a clear end point. Two of them are:
- “Where is authentication handled in this codebase?”
- “What are the main entry points and how do the key components fit together?”
The first is a good tour request because it names one concern. The second is broader, so use it as a way to find starting points, then narrow to a single behavior before tracing it.
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