From Out of Control to Stable Delivery: Practical Methods for AI Coding Agents

In software development, AI coding agents are often misunderstood as stronger code completion tools. A user states a requirement, the agent generates code, and the task is considered done. In real projects, however, code generation is only a small part of the work. The more important questions are how the agent understands the project, how it decides its next action, how it uses tools, and how it controls risk when it is uncertain.

Without explicit constraints, an AI coding agent can easily run into the following problems:

• It modifies files it should not touch.
• It does not follow the project’s existing coding standards.
• It delivers the surface functionality but breaks boundary conditions.
• It introduces new hidden risks just to pass local tests.
• It forgets early constraints as the context grows longer.
• When it encounters an error, it retries repeatedly without forming an effective diagnosis.

These symptoms do not necessarily mean the model lacks capability. More often, the way the work is organized has not been engineered. To make an agent work reliably, you first need to understand its basic operating mechanism.

1. An AI Coding Agent Is Fundamentally a Tool Loop

Many people imagine an AI coding agent as a “virtual developer” that has a complete map of the project. In practice, it usually does not understand the entire codebase at the start, and it does not generate a complete execution plan in advance.

A more accurate model is a continuously running loop:

1. Read the current context.
2. Analyze the user’s goal and the information available so far.
3. Select one or more tools.
4. Get the results from the tool execution.
5. Decide the next action based on those results.
6. Repeat until the agent considers the task complete or needs user input.

This can be represented with the following pseudocode:

while not finished:
    response = model(context)
    if response.requires_tool:
        result = execute_tool(response.tool_call)
        context.append(result)
    else:
        return response

The tools may include:

• Viewing directories and files
• Searching code or configuration
• Editing files
• Running build tasks
• Running tests
• Checking version differences
• Reviewing logs or diagnostic information

This pattern is often called the reasoning-and-action loop. The agent does not complete the whole task in one pass. It adjusts its next actions dynamically based on the new information from each round.

1.1 Why It Can Appear to Be “Out of Control”

An agent makes its decisions at runtime, not through a fixed process defined in advance. It might search for a keyword first or read a configuration file first. It might change the implementation first or run the tests first. Different contexts lead to different paths.

This creates three natural limitations.

First, the agent gathers information progressively. If it has not read a file, it cannot reliably determine the constraints in that file.

Second, the agent’s judgment depends on its context. If key information never enters the current context, the agent may make decisions based on incomplete facts.

Third, the agent’s default goal is usually to advance the task quickly, not to follow all of a team’s implicit rules. Naming conventions, testing requirements, layer boundaries, and off-limits areas are hard to infer reliably unless they are stated explicitly.

So “losing control” is usually not an occasional error. It is the expected result of working without project-level constraints.

2. First Principle: Write Project Rules into a Persistent Document

The most effective improvement is not repeating reminders in every conversation. It is creating a project rules file, for example at the root of the project:

PROJECT_RULES.md

The purpose of this file is to turn the stable, recurring, and important knowledge of the project into a specification the agent can read consistently.

It should contain at least the following.

2.1 Project Structure

Briefly describe what each main directory is responsible for, for example:

src/        Core business implementation
tests/      Automated tests
scripts/    Engineering helper scripts
docs/       Technical documentation
config/     Environment and build configuration

The directory descriptions do not need to be a full architecture design, but they should give the agent a basic map of the project. The following information is especially worth stating explicitly:

• Which directories contain the core implementation
• Which directories are maintained by generators
• Which files may only be modified manually
• Where test code and production code live
• Which fields in configuration files must not be changed directly

2.2 Build, Test, and Verification Methods

Do not just write “run tests after making changes.” Specify the order of tests and their scope, for example:

After finishing an implementation, run the quick tests for the relevant module first.
After changing a shared module, run the full test suite.
When an interface or data structure changes, run static checks.
When a test fails, analyze the cause first. Do not delete assertions or relax checks directly.

If the project has several types of tests, also explain what each one is for:

• Unit tests: verify individual functions or modules.
• Integration tests: verify how modules work together.
• Interface tests: verify inputs, outputs, and error handling.
• Regression tests: confirm that past bugs have not returned.
• Static checks: find type errors, formatting problems, and potential defects.

The closer the rules are to the actual workflow, the easier they are for the agent to follow. Abstract slogans are hard to apply.

2.3 Coding Standards and Design Boundaries

Project standards should not focus only on surface issues such as indentation and naming. They should also describe architectural boundaries, for example:

Business logic must not access the underlying storage directly.
External input must be validated at the boundary layer.
Changes to public interfaces must update all callers and tests.
Environment-specific configuration must not be hardcoded in business code.
Before adding a dependency, explain why it is needed and what alternatives were considered.

Rules like these noticeably reduce cases where code runs but violates the architecture.

2.4 Prohibited Actions

Many incidents happen because the agent does not know what it must not do. List these prohibitions separately:

• Do not modify keys, certificates, or production environment configuration.
• Do not delete failing tests to hide a problem.
• Do not reformat unrelated files on a large scale.
• Do not upgrade core dependencies without confirmation.
• Do not rename public interfaces.
• Do not commit temporary debugging code to the official implementation.
• Do not disable security checks to work around an error.

The value of a rules file does not come from its length. It comes from whether it covers high-risk decisions.

3. Layer the Rules to Avoid Information Overload

A common mistake is putting all knowledge into a single root-level rules file. A file that is too long causes two problems: the agent struggles to find the key points, and the rules may conflict with each other.

A layered approach works better.

3.1 Global Rules at the Root

The root file should contain only what every task must follow:

• The project’s goals
• The overall directory structure
• General testing requirements
• Security boundaries
• Commit and review requirements

3.2 Local Rules in Subdirectories

If different modules have different standards, place local documentation in the corresponding directories, for example:

src/
  PROJECT_RULES.md
tests/
  PROJECT_RULES.md

Local rules should describe only what is specific to that directory, such as how the interface layer handles errors, how test data is constructed, or naming conventions inside a module.

3.3 Resolve Conflicts with Priorities

The rules should state their priority order explicitly:

1. Security and compliance requirements come first.
2. The user’s explicit current goal comes next.
3. General rules at the root come after that.
4. Local subdirectory rules supplement the general rules.
5. When uncertain, pause changes and ask a question.

This prevents the agent from guessing how to balance competing requirements.

4. Give the Agent Clear Task Boundaries, Not Just the Desired Result

“Help me optimize this module” may be enough for a human developer, but it is usually too vague for an AI coding agent. A well-written task description should include the goal, scope, constraints, and acceptance criteria.

You can use a structure like this:

Goal:
Fix an error in a module that occurs in a specific scenario.
Scope:
Only modify the specified directory. Do not change public interfaces.
Current state:
The error occurs with a certain type of input. An existing test can reproduce it.
Constraints:
Keep the existing return format. Do not introduce new external dependencies.
Acceptance:
Add a regression test. Relevant tests pass. Unrelated files remain unchanged.

The key to this kind of description is not to spell out the whole implementation. It is to define what counts as done and what must not change.

4.1 Investigate First, Then Modify

For tasks of medium or higher complexity, split the process into two phases.

In the first phase, allow only reading and analysis:

• Find the relevant entry points.
• Trace the call chain.
• Confirm the data flow.
• Locate existing tests.
• Identify the potential scope of impact.

In the second phase, begin making changes:

• Propose a minimal change plan.
• State which files will be modified.
• Add or adjust tests.
• Run verification.
• Summarize the changes and remaining risks.

This reduces the chance that the agent starts coding too early, and it lets the user correct the direction during the planning stage.

5. Control the Scope of Changes with Small, Frequent Steps

One of the most common mistakes an AI coding agent makes is changing too much at once. Large changes make problems harder to locate, and they can mix unrelated refactoring into a feature change.

Follow these principles:

5.1 Solve One Core Problem at a Time

If the current task is to fix input validation, do not also restructure the directory layout, replace the logging framework, or unify formatting. Separate different goals into independent tasks.

5.2 Verify Immediately After Each Change

The ideal loop looks like this:

Read the current state
→ Modify one part
→ Run the relevant tests
→ Review the diff
→ Move on to the next part

Avoid editing dozens of files in a row and running the full test suite only at the end.

5.3 Prefer Minimal Patches

A minimal patch does not mean rejecting improvements. It means each change should relate directly to the problem being solved. The more focused the change, the easier it is to:

• Review
• Roll back
• Locate regressions
• Judge whether the tests are effective
• Assess the impact on other modules

6. Context Management Is Essential for Long Tasks

AI coding agents are usually limited by a context window. When a conversation grows longer, the system may compress the history, keeping only a summary or reloading only part of the project rules.

Therefore, do not rely on chat history to hold important constraints. Write the following information into persistent files:

• Architecture rules that must be followed
• Testing and build procedures
• Areas that must not be modified
• Acceptance criteria for the current task
• Design decisions that have been confirmed
• Known issues and items deferred for later

For long-running tasks, you can also maintain a short progress file:

TASK_STATUS.md

It might contain:

Completed:
- Located the input validation defect
- Added boundary condition tests
In progress:
- Adjusting the error return logic
Pending confirmation:
- Whether to keep compatibility with the old error codes

This way, even if the context is compressed, the agent can recover the task status by reading the file.

7. Rules Cannot Replace Permission Controls

A rules file expresses intent. It cannot fully replace technical safeguards. For high-risk projects, combine the rules with permission controls and environment isolation.

At a minimum:

• Run the agent in an isolated environment.
• Set read-only permissions on production configuration and sensitive directories.
• Require manual confirmation before high-risk operations.
• Keep credentials separate from the code repository.
• Keep audit records of file changes, command execution, and network access.
• Use version control to save every change so it can be rolled back quickly.

In particular, avoid exposing high-privilege credentials to the agent. Even without malicious intent, an agent can cause serious damage through misjudgment, a wrong path, or a faulty script.

8. Turn Testing from a Final Check into Continuous Feedback

Tests are not only a final judgment tool. They are also an important source of feedback that helps the agent understand the requirements.

When a test fails, do not simply tell the agent to “keep fixing it.” Ask it to complete a full diagnosis:

1. Which assertion failed?
2. What were the actual result and the expected result?
3. Did the failure come from the implementation, the test data, or the environment?
4. Could the current fix affect other scenarios?
5. Is a new regression test needed?

For new code the agent generates, pay particular attention to:

• Normal inputs
• Null values and missing fields
• Boundary values
• Repeated requests
• Timeouts and exceptions
• Insufficient permissions
• Concurrency or retry scenarios
• Compatibility with old data

Test coverage should measure business behavior and failure paths, not just lines of code.

9. Build a Stable Collaboration Template

To reduce communication costs, standardize common tasks into a template:

Task name:
Background:
Describe the business scenario in which the problem occurs.
Goal:
State the result you want to achieve.
Allowed changes:
List the directories or files that may be changed.
Prohibited changes:
List public interfaces, sensitive configuration, or unrelated modules.
Implementation requirements:
Analyze the current state first, then propose a plan. Do not expand the scope without confirmation.
Verification requirements:
Run the relevant tests and static checks, and describe any risks that remain uncovered.
Output requirements:
Summarize the changed files, the core logic, the test results, and the open issues.

The purpose of the template is to turn one-time experience into a team asset. As the template is used more often, you can add rules based on real incidents, which makes the agent’s behavior more consistent over time.

Conclusion: Stable Delivery Comes from Model Capability Plus Engineering Constraints

An AI coding agent is not an automatic machine that takes over the development process. It is a collaborative system that repeatedly observes, decides, and acts based on its context. Its value depends on three factors:

• Whether it has accurate project context
• Whether it has been told the goal, the boundaries, and the acceptance criteria
• Whether it works in an engineering environment that is auditable, testable, and reversible

The effective approach is not to keep repeating reminders to the agent. It is to write recurring knowledge into rules files, break complex work into small steps, require human confirmation for high-risk operations, and close the loop with tests and version control.

Once project rules are persistent, task boundaries are clear, change scope is controlled, and verification is standardized, an AI coding agent moves from occasionally generating code to completing engineering tasks reliably within defined constraints.