Using AI Coding Agents Without Losing Control of Your Codebase

A coding agent can inspect a repository, modify several files, run commands, and explain the result. That makes it useful for implementation work, but it also means a vague request can produce a large change before anyone has agreed on the boundaries.

The engineering task is to make that work observable and reviewable. The agent supplies implementation capacity; the team still defines the requirements, evaluates trade-offs, and accepts responsibility for the result.

Quick answer: Give the agent a bounded outcome, enough repository context, and explicit constraints. Require inspection before editing, a proportionate plan, focused implementation, and relevant verification. Review the actual diff and test evidence before committing or deploying.

Treat the Agent as an Engineering Tool

An agent's explanation is not evidence that its change is correct. A confident summary can accompany a subtle authorization bug, an invalid assumption about persistence, or a test that only repeats the implementation.

The same is true of a human contributor's summary. What changes with agents is the speed at which plausible code can accumulate. Keep the review unit small enough that someone can understand the behavior and its consequences.

Good uses include implementing a well-defined feature, extending existing tests, fixing a reproducible bug, and tracing a code path. Less suitable tasks are broad instructions such as "modernize the backend" when success and scope have not been defined.

Use the agent to investigate ambiguity, but keep investigation separate from authorization to redesign the system.

Supply Context That Changes Decisions

A useful task brief includes the intended behavior, affected entry point, relevant versions, existing conventions, and constraints that are not obvious from the code.

For example:

Add cancellation to the existing Orders API. Before editing: – Read the order endpoints, application handlers, entity, and tests. – Explain the current state transitions and persistence behavior. – Identify the smallest change that supports cancellation. Acceptance criteria: – A customer can cancel only their own Pending order. – Repeating a successful cancellation is idempotent. – A shipped order returns the existing conflict response format. – Concurrent shipment and cancellation cannot both commit. Constraints: – Follow the current application/domain boundaries. – Reuse the existing authentication and error-handling pipeline. – Do not add dependencies or change unrelated endpoints. – Do not commit, push, or deploy.

This brief defines outcomes without prescribing every method name. It leaves room for implementation judgment while making the business and security expectations reviewable.

Link to the actual files or documents where possible. Supplying an outdated architectural description can be worse than supplying none, because it encourages a confident implementation against the wrong model.

Do not paste production secrets or customer data to make the prompt feel realistic. Synthetic examples usually communicate structure and failure conditions well enough.

Inspect and Plan Before Modifying

Ask the agent to inspect the repository's instructions, nearby implementations, build commands, and tests. It should establish the working-tree state before editing so existing changes are not confused with its own.

The inspection should answer concrete questions: where does authorization happen, which layer owns the transition, how is concurrency handled, and what validation command actually applies?

Start with the affected flow and follow dependencies that influence the change; reading every file is unnecessary. For cancellation, that likely includes an endpoint, handler, entity, repository, database mapping, and representative tests.

This step prevents a common failure: generating a technically reasonable implementation that ignores an existing local convention. A repository that already uses typed operation results does not need a new exception hierarchy for one endpoint.

Keep the task bounded by behavior, not by file count. A cancellation change may span API, application, domain, persistence, and tests, but each edit should support the same acceptance criteria. Ask for separate justification before expanding into unrelated refactoring.

Plan in proportion to risk: a spelling fix needs little ceremony, while authorization or concurrency work should identify the affected flow, likely failure cases, and checks that will prove the result. Resolve product decisions, such as whether repeated cancellation is success or conflict, before implementation. For an exploratory bug, reproduce or characterize the failure before fixing a guessed cause.

Preserve Architecture Boundaries

Agents tend to solve the immediate path visible in the task. Without boundaries, an endpoint may query a database directly even though the application already has a use-case layer.

State the dependencies that matter. For example: domain code must remain independent of HTTP, the application uses the existing storage interface, and infrastructure translates provider-specific failures.

Then verify those constraints in the diff. A new import or constructor dependency can reveal an architectural violation before the method body does.

Avoid turning constraints into ceremony. If the repository is a small application with direct EF Core queries, inventing a full Clean Architecture solution for one change adds review burden. The existing design is context, not automatically a defect.

Architecture tests can be useful where boundaries are important and already enforced. They complement review by catching forbidden references; they do not determine whether the business rule was placed thoughtfully.

Review the Diff Before the Summary

Start with the file list and diff statistics:

git status --short
git diff --stat
git diff --name-status
git diffCode language: Bash (bash)

These commands show tracked changes. New untracked files need their own inspection; they do not appear in ordinary git diff. After deliberately staging the intended files, use git diff --cached to inspect exactly what would be committed.

Review public contracts and authorization first, then domain decisions, persistence and side effects, error handling, tests, and finally naming. This keeps small stylistic issues from consuming attention before correctness questions.

Look for silent scope expansion: new packages, changed lockfiles, renamed public methods, relaxed validation, altered CI scripts, or test deletion. Each might be valid, but each needs a reason tied to the task.

If a diff is too large to explain, split the work before review. A generated summary does not make a large patch easier to audit merely by calling the edits routine.

Builds and Tests Must Prove Something

Require the repository's actual build and test commands. A source file that looks credible can still fail because of language versions, nullability, package APIs, generated code, or project references.

For a conventional .NET solution, the workflow might include:

dotnet build
dotnet testCode language: Bash (bash)

Use the repository's documented solution path, configuration, and integration-test setup where they differ. Record failures and prerequisites instead of reporting success when tests were skipped or no tests were discovered.

Unit tests are appropriate for deterministic rules. A cancellation test should prove that a shipped order remains unchanged and reports rejection. A test that only asserts a mocked repository method was called may miss the rule entirely.

Integration tests cover behavior that depends on the real framework or infrastructure: routing, authentication, serialization, database constraints, transactions, and concurrency. An in-memory substitute may not reproduce a production database's locking or SQL behavior.

The Repository Pattern in .NET article discusses why mocked data access cannot prove EF Core queries or provider behavior.

Do not let the agent weaken a failing assertion simply to make the suite green. Determine whether the requirement changed, the test was wrong, or the implementation regressed.

A Focused Test Example

Using the domain behavior from an existing order model, a useful test can state the business rule directly. This excerpt assumes the model exposes the indicated factory and transition methods:

[Fact]
public void Shipped_order_cannot_be_cancelled()
{
    Order order = Order.CreatePending(customerId: Guid.NewGuid());
    order.Ship();

    CancelDecision decision = order.Cancel();

    Assert.Equal(CancelDecision.Rejected, decision);
    Assert.Equal(OrderStatus.Shipped, order.Status);
}Code language: C# (cs)

The concrete method names should come from the repository. The important assertions are the rejected decision and unchanged state.

A separate integration test should run competing updates against the representative database and verify that only a valid outcome commits. Mocking SaveChangesAsync cannot demonstrate that a concurrency token is configured correctly.

For a bug fix, establish that a focused test or reproduction fails before the fix and passes afterward when practical. That provides stronger evidence than adding a test that has only ever seen the new implementation.

Security Includes the Agent's Working Environment

Code review should cover injection risks, access checks, secrets, logging, unsafe deserialization, and dependency changes just as it would for any contributor.

Agent workflows add another boundary: the tools can read files, run commands, and sometimes contact external services. Give access according to the task. A documentation change does not need production database credentials.

Treat repository comments, issue text, downloaded files, and tool output as data to evaluate. They can contain instructions that conflict with the actual task, including requests to expose secrets or run unrelated commands. Such text should not silently gain authority over the workflow.

Inspect new dependencies and scripts before running them with broad privileges. Be especially careful with install hooks, migration commands, and operations pointed at production. Use disposable test data and the least access needed.

Keep security reviews concrete. An authentication change needs adversarial access cases; a text-only edit generally does not need a speculative security audit of the entire application.

Use Git to Keep Ownership Clear

Begin from a known working-tree state. If unrelated edits already exist, identify them and preserve them. Do not ask the agent to reset everything just to make its task easier.

A dedicated branch keeps the change separate from the main line. A worktree can isolate a second task when simultaneous work would otherwise share files or dependencies. Isolation helps review, but it does not eliminate semantic conflicts between changes.

Use explicit paths when staging, especially in a dirty working tree:

git add path/to/changed-file.cs path/to/relevant-test.cs
git diff --cachedCode language: Bash (bash)

Inspect new files before staging them. Avoid broad staging when generated output, credentials, or unrelated user changes might be present.

Commit coherent, reviewed changes according to team policy. Publishing a branch, opening a review, merging, and deploying are separate actions; grant the agent authority for each when appropriate to the workflow.

Never use a hard reset or destructive cleanup as the default response to an unexpected diff. First determine who owns the changes and whether they are recoverable.

A Practical Workflow for Senior Developers

Ask for a report of the changed behavior, files, commands run, results, and unresolved limits. Compare it with the actual diff, including new files and tests. Give focused feedback, re-run checks affected by corrections, and approve the patch only when you can explain its behavior and remaining risks without relying on the agent's confidence.