From Uncontrolled to Governable: Engineering Practices for AI Coding Assistants

Many developers go through a similar experience when they first use an AI coding assistant. They are initially impressed by its ability to generate code, explain a project, and execute commands. Over time, however, the results can become increasingly difficult to control.

Ask it to modify only three files, and it may spread changes across a dozen. Request a small feature, and it may also refactor a core module. Specify a coding style, and a few rounds of conversation later it may return to its default format. Tell it not to change dependency configuration, and it may still “optimize” related files.

When the scope of changes expands and rules stop being followed, developers often conclude that the tool is not intelligent enough.

A more accurate explanation is that the problem is not entirely the model’s capability. The project is missing engineering constraints.

1. Understand How AI Coding Assistants Work

Many people think of an AI coding assistant as an enhanced code-completion tool: the user describes a requirement, the system generates code, and the task is finished. In practice, these tools usually operate through a more complex execution loop.

A typical workflow can be abstracted as follows:

1. Receive the user’s objective.
2. Analyze the current context.
3. Read files, search code, or inspect configuration.
4. Decide what to do next.
5. Call editing, execution, or testing tools.
6. Receive the tools’ output.
7. Reevaluate the next step based on the result.
8. Continue until the task is completed, fails, or is stopped by the user.

This is a reasoning–action–observation loop. The model does not necessarily generate a complete solution in one pass. Instead, it reassesses the next action after each operation.

As a result, the final outcome depends not only on the model itself, but also on what it can see, what it is allowed to do, and whether the project contains clear rules.

Context Is Not Permanent Memory

Most AI coding assistants have limited context windows. As files are read, commands are executed, and requirements are discussed, earlier information may be compressed, summarized, or removed.

This means that:

• Rules stated only in conversation may be forgotten during a long session.
• Previously defined file boundaries may no longer be followed accurately.
• Temporary agreements cannot be reused reliably.
• A new session generally does not automatically inherit the project context from the previous one.

For this reason, critical constraints should not exist only in chat messages. Project conventions, prohibited operations, testing requirements, and delivery standards should be stored in project documentation that the assistant can read consistently.

2. Create a Project-Level Instruction File

A practical approach is to create an instruction file in the repository root, such as PROJECT_GUIDE.md.

Its purpose is not to replace all development documentation. Instead, it tells the AI coding assistant how the project is organized, which rules it must follow, and what verification steps are required before a task is considered complete.

What to Include in the Instruction File

A useful instruction file should contain at least the following sections.

Project Overview

Describe the technology stack, major directories, and core modules. For example:

• src/: application source code
• tests/: automated tests
• scripts/: development utilities
• docs/: design and usage documentation
• config/: environment and build configuration

The clearer the directory structure, the less likely the assistant is to modify the wrong location because of an incorrect assumption.

Coding Standards

Do not simply write “follow the project standards.” State the rules explicitly:

• Which indentation style should be used?
• Are semicolons required?
• Which naming conventions apply?
• How should errors be handled in asynchronous code?
• Must public interfaces include type definitions?
• Should comments explain reasons or implementation steps?
• Are new dependencies allowed?

The more specific the rules are, the more consistently they can be applied.

Modification Boundaries

This is one of the most important sections for controlling risk. Specify:

• Which directories may be modified by default;
• Which files must not be changed without confirmation;
• Whether changes to databases, permissions, deployment, or dependency configuration require approval first;
• That existing tests must not be deleted;
• That test assertions must not be changed merely to make tests pass;
• That unrelated refactoring is not allowed.

Avoid vague instructions such as “modify core files carefully.” The word “carefully” is difficult to enforce. Replace it with conditions that can be evaluated.

Verification Workflow

Require the assistant to perform a fixed set of checks after making changes. For example:

1. Review the list of changed files.
2. Run formatting checks.
3. Run static analysis.
4. Run tests for the affected modules.
5. Report any checks that were not run and explain why.
6. Provide a summary of changes before delivery.

This turns “the code has been written” into “the change has been verified and is ready for review.”

Manage Rules in Layers

Large projects should not place every rule in a single file. A layered structure may include:

• A root-level file for global conventions and general safety boundaries;
• Subdirectory files for module-specific conventions;
• Feature-specific files for local implementation requirements;
• Separate documentation for detailed designs, interface specifications, and business context.

This reduces irrelevant context and prevents the assistant from applying a special rule from one module to the entire project.

Rules Must Be Executable and Verifiable

Effective rules have three characteristics:

• Clarity: Avoid vague terms such as “appropriately,” “if possible,” or “when necessary.”
• Verifiability: Use commands, tests, or file checks to confirm compliance.
• Priority: State which rules take precedence over ordinary development convenience.

For example, instead of writing “Do not change dependency configuration,” write:

This task must not modify dependency manifests or lock files. If a new dependency is genuinely required, first list the package name, purpose, and alternatives, then wait for approval before making the change.

3. Divide Work into Three Modes

Different tasks require different levels of authorization. A useful approach is to divide the workflow into three modes.

Observation Mode

Observation mode permits reading and analysis but prohibits file modifications and high-risk commands. It is suitable for:

• Taking over an unfamiliar project;
• Investigating a defect;
• Estimating the scope of a refactor;
• Preparing a technical proposal;
• Analyzing potential impact.

At this stage, ask the assistant to provide:

• Its understanding of the project structure;
• Relevant code paths;
• Possible causes of the current issue;
• Files it proposes to modify;
• Risks and alternative approaches.

Analyzing first and executing later can significantly reduce accidental changes.

Implementation Mode

Implementation mode permits modifications within a clearly defined scope while preserving the project’s boundaries. A task description should include:

• The target feature;
• Directories that may be modified;
• Files that must not be changed;
• Acceptance criteria;
• Tests that must be run;
• Items that are explicitly out of scope.

A well-defined task should allow the assistant to answer three questions: What should I change? What must I leave untouched? How can I prove that the change is correct?

Review Mode

Review mode focuses on examining existing changes rather than continuing to write code. The review should check:

• Whether the changes exceed the task scope;
• Whether unnecessary refactoring was introduced;
• Whether error-handling gaps exist;
• Whether compatibility was affected;
• Whether tests or documentation are missing;
• Whether sensitive information may have been exposed.

Separating implementation from review helps prevent the assistant from rationalizing its own generated code while overlooking actual defects.

4. Use Hooks, Skill Templates, and Specialized Agents

Project instruction files are only one part of the solution. Three additional mechanisms can improve consistency.

Hooks: Automate Safety Checks

Hooks are checks that run automatically when specific events occur. Examples include:

• Checking whether a file path is within the allowed scope before modification;
• Blocking deletion, overwriting, or bulk-edit commands before execution;
• Running formatting checks when a task ends;
• Checking test results and sensitive information before submission;
• Requiring human confirmation when dependency files change.

Hooks are useful for rules that must always be enforced. They create process-level protection instead of relying on the model to remember a reminder.

Skill Templates: Standardize Repeated Workflows

For recurring tasks, create reusable skill templates such as:

• Adding an API endpoint;
• Fixing a defect;
• Writing unit tests;
• Migrating a data structure;
• Generating change notes;
• Performing a code review.

A template can define the required inputs, operation sequence, output format, and verification steps. Team members do not need to describe the workflow from scratch each time, and the assistant receives more consistent guidance.

Specialized Agents: Narrow the Scope of Responsibility

Complex projects can assign different responsibilities to different specialized agents:

• Analysis agent: Locates problems and proposes solutions;
• Implementation agent: Modifies only the approved scope;
• Testing agent: Adds tests and runs verification;
• Review agent: Checks quality, security, and compatibility;
• Documentation agent: Maintains interface and change documentation.

The main value of specialized agents is not simply running multiple agents at once. It is reducing the responsibility range of each executor. The narrower the responsibility, the easier it is to define clear inputs, outputs, and acceptance criteria.

5. Establish a Controlled Interaction Workflow

The following closed-loop process is recommended.

Step 1: Ask the Assistant to Restate the Goal

Require it to describe:

• Its understanding of the requirement;
• The files it plans to read;
• The files it plans to modify;
• Potential risks;
• Information it still needs.

If the scope is already wrong during this restatement phase, correct it immediately instead of waiting until the code has been written.

Step 2: Require a Plan Before Execution

For cross-file or high-risk tasks, ask for an implementation plan before allowing changes. The plan should include:

• The sequence of changes;
• The file list;
• The testing strategy;
• The rollback approach;
• Items that are outside the current task scope.

Step 3: Make Changes in Batches

Do not authorize broad changes all at once. Proceed with one module, one objective, and one verification cycle at a time. After each batch, inspect the diff and test results.

Step 4: Force a Diff Review

When the task is complete, check:

• Whether the number of modified files matches expectations;
• Whether unrelated formatting changes appeared;
• Whether protected areas were modified;
• Whether unnecessary dependencies were added;
• Whether existing logic or tests were removed.

Step 5: Record the Result

The final report should include:

• Completed items;
• Modified files;
• Test commands and results;
• Incomplete items;
• Potential risks;
• Recommended follow-up work.

This record supports human review and makes it easier to continue work in a later session.

6. Common Mistakes and Better Approaches

Mistake 1: Focusing Only on Prompts Instead of Project Rules

Temporary prompts are useful for describing the current task, but they are not suitable for carrying long-term project conventions. Stable requirements should be stored in project-level configuration and version-controlled.

Mistake 2: Granting Too Much Authorization at Once

Instructions such as “optimize the entire project” are too broad and can trigger uncontrollable changes. Break the work into smaller tasks with measurable verification criteria.

Mistake 3: Reviewing Only the Final Code

Looking only at the final result can hide unauthorized changes, unrelated refactoring, and other risks. Review the operation history, change list, and testing process as well.

Mistake 4: Treating the Assistant as a Fully Autonomous Developer

An AI coding assistant can accelerate analysis and implementation, but it cannot replace requirements confirmation, architectural decisions, or final acceptance. The more a task involves core data, permissions, or production environments, the more important human approval becomes.

Conclusion

Moving an AI coding assistant from “uncontrolled” to genuinely useful is not primarily about writing increasingly complicated prompts. It requires a complete set of engineering constraints:

• Use project instruction files to preserve project knowledge;
• Use observation, implementation, and review modes to control authorization;
• Use hooks to automatically intercept risks;
• Use skill templates to standardize repeated workflows;
• Use specialized agents to separate responsibilities.

When rules can be read consistently, tasks can be divided and verified, and risks can be checked before and after execution, an AI coding assistant becomes more than a code-generation tool. It becomes a development collaborator that follows project conventions, understands team processes, and delivers results more reliably.