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Use a repository-aware AI coding agent as a research assistant, not as the authority on your system. Start with one observable behavior, ask for a file-and-symbol map, trace a concrete input to its result, require references and uncertainty labels, then verify every important claim against the current source and tests. Keep the investigation read-only until you understand the path, and preserve only verified findings as project documentation.
What AI can—and cannot—tell you about legacy code
Legacy code is difficult to change because its behavior is distributed across old entry points, configuration, shared utilities, database calls, external services and tests that may document only part of the contract. An AI agent can make that landscape searchable and explain likely control flow in plain language. It can combine semantic search, exact text or regular-expression search, symbol-usage tracing, directory listings and file reading to assemble a working map.
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That map is a hypothesis. Microsoft Visual Studio Code documentation puts the boundary plainly: “Treat its explanation as a starting point to check against the code, not as an authoritative description of the project.” A fluent explanation can still point to a generated copy, miss a caller, confuse a fixture with production code or infer behavior that no longer exists. Your job is to use the agent to reduce the cost of finding evidence, then make the evidence decide.
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- “Where is authentication handled?”
- “Walk me through how the payment retry logic works.”
- “Summarize how requests flow through this service from entrypoint to response.”
These examples appear in official guidance from Microsoft, Anthropic and OpenAI. They are useful because each names a behavior that can be followed from an input to an outcome.
Prepare a safe, narrow investigation
Choose one observable behavior
Pick a route, command, UI action, error message or domain behavior that you can recognize from outside the code. Define the question in terms of one input and one expected result. For example, investigate what happens when a customer submits a payment and the provider times out, rather than attempting to understand the entire billing subsystem.
Write down the boundary of the investigation before you prompt:
- the behavior or error to explain;
- the repository or service being examined;
- the concrete input, request, command or event;
- the result you want to account for;
- directories that are in scope and known generated, vendor or fixture directories to avoid.
This gives you a completeness test. You can ask whether the agent accounted for the path between the chosen input and result without allowing an unrestricted tour of unrelated code.
Keep permissions and secrets out of the experiment
Begin with read-only access. Do not ask an agent to edit files, install dependencies, run arbitrary scripts or reveal secret values simply to explain a code path. A prompt saying “do not edit” is not itself a permissions boundary; review the agent’s configured permissions and tool activity.
Dependency installation or application startup is not required just to begin reading source in the workflow described by the VS Code guide. If runtime confirmation becomes necessary later, inspect the project’s setup instructions and proposed test command first. Record what actually ran and in which environment; do not imply that a test passed merely because an agent suggested it.
Step 1: Request a small code map
Your first prompt should produce an inventory, not a speculative essay. Ask for the entry point, implementation modules, configuration, call sites, external boundaries and relevant tests. Require a reason for every file so the list remains reviewable.
We are investigating one behavior: <describe the route, command, UI action, or error>.Build a small code map. Identify:1. The entry point for this behavior.2. The implementation functions or classes that handle it.3. Configuration and feature flags that can change the path.4. Callers and important downstream dependencies.5. Database, queue, network, filesystem, or other external boundaries.6. Tests that assert the behavior or its edge cases.For every file, give its path, relevant symbols, and why it belongs.Exclude generated files, build output, logs, vendored dependencies, and secrets.Ask the agent to say when it cannot verify a dependency. A useful map distinguishes an actual implementation from a generated artifact or a test double. It also makes omissions visible: if no caller, return path or test was found, you have a precise follow-up instead of a vague feeling that the explanation is incomplete.
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Step 2: Trace one concrete path from input to result
Once you have a map, provide one realistic input and ask the agent to follow it in order. Require the names of functions or symbols, what enters and leaves each step, branch conditions, error handling and service boundaries.
Trace this specific case from entrypoint to result:Input: a payment retry request for order 1842 after the provider returns a timeout.For each hop, report:- file path and symbol;- values or objects entering and leaving;- branches, defaults, and retry limits;- database, queue, or external-service calls;- errors that are transformed, logged, retried, or returned;- the final response or side effect.Mark every statement as checked fact, inference, or open question.Concrete tracing prevents a common failure mode: an agent describes the “main” path while silently skipping a default, feature flag, early return or exception handler. If a dependency is unavailable, the answer should state that it cannot verify the connection rather than filling the gap with a plausible story.
Ask for missing connections, not a new repository tour
Follow up narrowly. Examples include:
- “You named
RetryCoordinator.handle; show its caller and the value returned to the HTTP handler.” - “Where is the default retry count set when the configuration key is absent?”
- “Which assertion proves that a timeout is retried rather than returned immediately?”
- “You cited a generated client. Locate the active wrapper that calls it.”
A targeted question keeps the context focused and gives you a falsifiable item to inspect.
Step 3: Demand references and separate certainty levels
Require file paths and symbols for every material claim. Ask the agent to distinguish three categories:
- Checked fact: directly visible in the current checkout, such as a call expression, condition or test assertion.
- Inference: a reasonable interpretation that connects observed code but is not explicitly stated.
- Open question: a dependency, runtime setting or branch the available files do not establish.
Have the agent revise its account when a link is missing. If it names a return value without showing the caller, ask for the caller. If it describes a database write without identifying the transaction boundary, ask for that boundary. This discipline turns an attractive narrative into a chain of claims you can audit.
Step 4: Verify the explanation against the repository
Inspect the cited implementation
Open every important file yourself. Confirm that the referenced symbol exists in the current checkout, that the lines are active implementation code, and that the path is not a generated copy, fixture, example or stale branch. Check callers and returned values in both directions: an explanation is incomplete if it shows where a function is called but not how its result is consumed.
Compare documentation and configuration
Read project documentation, configuration files and deployment settings that can alter the path. A feature flag, environment default or routing rule may redirect execution before the function the agent identified. Treat comments as useful context, not proof that the running code still behaves as described.
Read test assertions, not just test names
Inspect the setup, input, assertions and boundary cases in each cited test. A test named retries payment does not prove that it covers provider timeouts, maximum attempts or idempotency. A test is evidence of an asserted contract only when its assertions match the behavior under investigation.
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Do not claim an unrun test passed
Source inspection and test execution are different kinds of evidence. If you did not run a test in the relevant environment, say that the test exists or that its assertions appear to constrain the behavior; do not report a passing result. If runtime confirmation is needed, review setup, proposed commands and permissions before running anything, then record the actual command, environment and outcome.
Step 5: Keep the investigation read-only until the path is clear
Understanding should precede modification. A read-only pass lets you detect missing context without introducing changes that make later evidence ambiguous. Ask the agent to explain code, locate references and identify tests before granting edit or execution authority.
When a runtime check is justified, use the smallest safe operation: a documented test or narrowly scoped diagnostic in an approved environment. Review tool activity and outputs for secret exposure. Never paste credentials into a prompt to help the agent “complete” a trace, and do not assume that broad repository access is necessary for a question about one behavior.
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How repository-aware agents gather context
Different agents expose different combinations of repository search and tracing, but the useful capabilities are similar:
| Capability | What it contributes | What you still verify |
|---|---|---|
| Semantic search | Finds code related by meaning when names differ or terminology is inconsistent. | Whether the result is active code and actually participates in your chosen path. |
| Exact text or regular-expression search | Locates literal error messages, configuration keys, event names and string-based routes. | Whether the occurrence is production code, documentation, a fixture or generated output. |
| Symbol and usage tracing | Connects definitions, callers and references across modules. | Whether dynamic dispatch, reflection, plugins or generated bindings create additional paths. |
| Directory and file reading | Reveals conventions, configuration, tests and neighboring implementations. | Which files are authoritative for the deployed edition or service. |
Microsoft’s VS Code documentation describes agents combining these strategies and choosing among them for workspaces of different sizes. No single search mode is complete: semantic search can miss a literal configuration key, while exact search can miss a behavior expressed through different names.
Reduce context pollution
Generated files, build artifacts, logs and large datasets can flood the context with irrelevant text, consuming tokens and making a relevant match harder to notice. Configure exclusions where appropriate. An exclusion has a cost: it can also explain why a search found nothing. When a dependency appears to be missing, check exclusion rules before concluding that the code is absent.
Project instructions: useful conventions, not universal magic
Persistent instructions can tell an agent which directories are authoritative, how tests are organized, which commands are safe and what style the project follows. In the VS Code workflow documented by Microsoft, examples include .github/copilot-instructions.md for Copilot, CLAUDE.md for Claude and AGENTS.md for Codex. These filenames are harness-specific examples, not a guarantee that every tool or repository loads them.
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Turn verified findings into durable documentation
Do not preserve the agent’s first narrative verbatim. After checking the source and tests, write a compact map that another developer can use without repeating the entire investigation.
A useful map contains:
- the behavior and the concrete input investigated;
- the entry point and the ordered symbols on the verified path;
- important inputs, outputs, defaults and feature flags;
- database, queue, network and other external boundaries;
- error, retry, timeout and fallback branches;
- tests that assert the contract, including notable edge cases;
- build and test commands documented by the project;
- directories to avoid because they contain generated or unrelated material;
- open questions that remain unverified.
Store the map where maintainers will look for architecture or onboarding information. Add only concise instructions that solve an observed problem, and update the map when a verified change invalidates it. Documentation is valuable here because it records relationships that are difficult to infer from isolated files; it should not pretend that an untested inference is a guarantee.
Prompt patterns you can reuse
Locate a behavior
Find where this behavior is implemented: <behavior>.Start with entrypoints and exact user-visible strings, then use semantic and symbol searches.Return the smallest relevant file map with paths, symbols, callers, external boundaries, and tests.Explain control flow
Walk through <behavior> for this input: <concrete input>.Show each function or method in order, the value passed in and returned, branches taken, defaults, errors, and side effects.Separate checked facts from inferences and list anything you cannot verify.Find the constraint on a change
I may need to change <behavior>. Which tests, callers, configuration values, and external contracts constrain it?Quote or summarize the relevant assertions and identify boundary cases that are not covered.Audit an earlier explanation
Review the previous trace against the current checkout.For every claim, provide the file and symbol that proves it, identify stale or generated references, and correct missing callers, return paths, defaults, or error branches.Comparing documented agent workflows without overclaiming
Official product documentation describes several workflows for unfamiliar-code exploration. It does not provide a controlled benchmark showing that one agent is more accurate, faster or better value than another. Compare the practical controls that matter for your repository instead of assuming a winner.
| Workflow described in official guidance | Useful focus for a legacy-code investigation | Evidence boundary |
|---|---|---|
| Microsoft Visual Studio Code agent guidance | Workspace context, semantic and text search, symbol tracing, exclusions, and checking explanations against source. | Documentation guidance, not a measured accuracy study. |
| Anthropic’s Claude Code use-case guidance | Tracing a module or function, explaining control flow, branches, edge cases and connections; its April 15, 2026 example asks, “walk me through how the payment retry logic works”. | A documented use case, not a comparison test. |
| OpenAI Codex engineering guidance | Locating core feature logic, mapping relationships, tracing data flow and identifying missing architecture documentation. | Vendor capability description, not proof of superior results. |
When selecting a workflow, ask whether it can access the required repository context, show source files and symbols, trace callers and data, respect permission controls, load concise project instructions and fit tests into the review process. Prices, plan availability and regional access are separate questions and are not established by these workflow descriptions.
Failure modes and recovery steps
Plausible but unsupported explanation
Symptom: the answer sounds coherent but contains few paths, symbols or assertions.
Recovery: require a claim-by-claim citation, classify each statement as fact, inference or open question, and inspect the implementation yourself.
Incomplete path
Symptom: the agent identifies an entry point but skips a caller, return value, branch or service boundary.
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Recovery: ask for the missing connection narrowly and request a concrete input-to-output trace rather than restarting with a repository-wide prompt.
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Stale or misleading evidence
Symptom: a symbol exists only in generated output, a fixture or an inactive copy.
Recovery: verify the current checkout, locate the active caller and compare the cited path with deployment and build conventions.
Misread tests
Symptom: the agent treats a test name as proof or claims a test passed without an execution record.
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Context pollution
Symptom: search results are dominated by logs, generated code or large data files.
Recovery: apply appropriate exclusions, then check those exclusions when a seemingly expected dependency cannot be found.
Unsafe or overbroad action
Symptom: the agent proposes edits, installs dependencies, runs unfamiliar code or requests secrets before the behavior is understood.
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Why this method fits legacy-code work
The central legacy-code problem is often not syntax; it is insufficient understanding to make a safe change. Michael Feathers’s Working Effectively with Legacy Code, listed by Pearson as a first-edition paperback (ISBN-13 9780131177055), includes “I Don’t Understand the Code Well Enough To Change It” and chapters on feedback, tests and safe changes. That book is not an AI manual, but its framing explains why a verified behavior map is valuable: before changing a brownfield system, you need observable seams and feedback that distinguish a safe alteration from an accidental regression.
AI helps with the reconnaissance part—finding relationships and translating unfamiliar code—but it does not remove the need for feedback. The repository, its active configuration and its assertions remain the authority you must consult before editing.
A compact checklist for your next investigation
- Choose one observable behavior and one concrete input.
- Define the in-scope repository area and exclusions.
- Ask for an entry-point, implementation, configuration, caller, boundary and test map.
- Request a step-by-step trace with values, branches, errors and side effects.
- Require file paths, symbols and explicit uncertainty labels.
- Inspect the cited code in the current checkout.
- Confirm callers, return values, active configuration and test assertions.
- Keep the process read-only until the behavior is understood.
- Run a documented test only when runtime confirmation is needed, and record what actually ran.
- Save a concise, verified map and project-specific instructions where maintainers can find them.
This sequence makes an AI agent useful without granting it authorship of your system’s meaning. It turns an unfamiliar repository from a wall of files into a set of checkable claims, while leaving final interpretation—and any decision to change code—with the developer who verified the evidence.
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