I wanted my coding agent to remember the codebase

An AI coding agent can look fast on its first task. The expensive part often appears on the fifth.

It opens the same files again. It traces the same call chain. It rediscovers where a value is assembled, and it can still miss one screen or one boundary that needs to change. The cost is not only tokens. A change takes longer, and the reviewer has more places where an incomplete patch can hide.

I wanted to give the agent a useful kind of working memory: a reusable map of the codebase that makes repeated investigation cheaper without making the next product change less safe.

That last condition changed the whole experiment. A short answer is a saving only when the resulting patch is correct. If an index helps the agent find four files quickly but it misses the fifth file that carries the contract, the saved tokens simply become rework.

This article is about how I tested that idea. It is not a leaderboard for code graph tools. My aim was more practical: learn when a saved code map earns a place in day-to-day product work, and when current source and tests must take over.

An agent repeatedly needs answers to a small set of questions:

Tools answer these questions in different ways. Some ask the same language services used by an IDE. Others keep a parsed map of symbols, imports, calls, and tests. I call that map an index in this article. It is saved navigation, not a replacement for the source of truth.

The promise is attractive. The agent should spend less time reopening code and more time making a useful change. But the promise only matters if quality stays level or improves.

I did not count a small response, a convincing explanation, or a passing frontend check as success. A patch had to satisfy a source-derived behaviour contract. Its focused test had to pass. Then the same test had to fail again after I deliberately reintroduced a relevant defect.

That last step matters. It checks that the test is able to catch the mistake we care about, such as a wrong calculation or an update missing from the mobile layout. A green test that does not fail when the defect returns gives false confidence.

I used an LLM as a judge in an earlier exploration to help decide which questions were worth investigating. I do not use an LLM score to decide whether a patch is correct. A model can prefer a short, plausible answer that names the wrong function. For the change tasks below, source checks and deliberate mutations are the final gate.

I did not start by ranking tools. I first looked at different ways an agent might keep code context between questions: ordinary source search, context packers, language-service bridges, graph indexes, and semantic search. They do different jobs, so one headline score would hide more than it explains.

The work then narrowed in three stages:

The broad first stage gives examples of failure modes. The narrow third stage checks a real outcome. Neither is a universal ranking or a token-saving claim. Together they answer a practical question: which recurring question can a tool help with in this repository? What must I still verify? Does it lower total work without lowering patch quality?

The selection framework keeps the candidate history. The reproducibility manifest has pinned revisions and raw records. I keep that detail in the archive so the article can explain the decision without asking every reader to audit a tool catalogue first.

I ran three fixed change tasks in one private Python and TypeScript product. Each condition used five fresh agent sessions. I compared ordinary source navigation with Code Review Graph and Serena, two tools that give an agent structured help finding code relationships.

Before each batch, I froze the task and an independent evaluator. A known-good patch had to pass. An untouched fixture and an incomplete patch had to fail. Only then did I count fresh agent runs.

The last row was the useful surprise. I expected a structured map to help most on the hard task. Instead, many patches looked plausible but were incomplete: a paginated field was absent, a closed vocabulary was changed incorrectly, or an uncertain state was left unprotected.

This does not mean that one condition is better than another. Five out of five still has a wide exact 95% interval, from 47.8% to 100%. These are small, task-specific observations. They do show something important for tool choice: an index can give an agent a faster starting point, but it does not supply a missing product contract or prove that every layer was changed.

I discarded an early UI batch. An old inverse patch was still visible in Git, so it could have given an agent the solution without requiring it to understand the current code. I rebuilt a clean one-commit fixture, reran the controls, and counted only the 15 fresh runs. The lesson is simple: a benchmark must not quietly provide its own answer.

The quality-gate summary and redacted UI task record explain the method and its limits. They do not measure token saving, elapsed time, index build cost, refresh cost, full browser behaviour, or general agent quality.

The product source is private, so these task records are deliberately redacted. They let a reader inspect the behaviour contract, controls, and evaluator boundary, but they are not a package that an outside reader can rerun exactly. That limits their weight, and is one reason I do not use them to claim a winner.

The change tasks tell me whether the final patch survives a quality gate. I also checked four smaller retrieval questions against fixed source versions. This matters because an agent can act on a compact answer long before a test has a chance to correct it.

Ktor is an open-source Kotlin framework for building server applications. This case shows why a small answer can be dangerous. The low-level parseHeaderValue function has one direct caller, parseHeaders. The tool also returned 16 callers of a different public function with the same name. The response was compact, but almost all of it was wrong for the target I asked about.

This is not an argument against a graph or language tool. It is a reason to give the answer a trust contract: the exact target, the indexed scope, a clear test boundary, and a label for exact match, possible match, or no match.

I then checked what happened after source changed. In a controlled FastAPI case, an index was built when the helper solve_dependencies had four direct callers. I added a fifth caller in a local source revision. The existing indexes still reported yesterday’s relationships until their refresh path ran.

I ran one small rename task with ordinary source navigation, Graphify with the old graph, and Graphify rebuilt after the change. Each condition passed the same deterministic evaluator once. The stale-graph run still succeeded because the agent searched current source before editing all five callers.

That is a workflow observation, not a performance result. The control was small, each condition ran once, and the baseline passed too. It supports one rule only: use a saved map to start the search, then verify a relationship-sensitive change against current source. The normalized control record has the precise boundary.

Vendor demonstrations are useful for discovering possibilities. They cannot tell us whether a tool fits a particular architecture, build, codebase age, or team workflow. The test can be small and still be much more useful than a generic ranking.

I turned the method into three small, tool-neutral companion skills:

They do not make an index correct. They make its scope, freshness, and uncertainty visible before the agent acts.

The real value of persistent context will not appear in a single answer. It will appear, if it appears at all, across a sequence of independent tickets: the agent asks fewer repeated questions, reuses safe navigation, and still delivers patches that meet the same quality gate.

That next experiment needs a baseline and an indexed condition on comparable tickets. It should measure the whole lifecycle: setup, index build, refresh, tool calls, retries, context, elapsed time, and patch quality. Quality comes first. Only after it stays level can lower overhead become a useful result.

My conclusion is deliberately modest. Persistent code context is worth trying as working memory for an agent. It can make repeated navigation easier. It does not make the agent understand a product automatically, and it cannot replace a clear behaviour contract, current source, or tests that can expose a regression.

The public evidence archive includes: What does your coding agent keep re-investigating in the same repository?