1. Starting from a Common Misconception

Many developers see AI coding assistants as “super autocomplete tools”: you give an instruction, the AI writes some code, and that is all. In practice, things often go wrong. The AI edits files it should not touch, ignores the team’s coding standards, writes tests that do not meet project requirements, and every new session requires explaining the project background from scratch.

The root cause is not insufficient model capability. Users often do not understand how these tools work internally. This article examines general-purpose AI coding assistants, especially terminal-based agent tools, explains their operating mechanism, and presents an engineering approach for moving AI from “out of control” to “controllable and usable.”

2. Understanding the Underlying Mechanism: A Minimal Loop

2.1 The Core Architecture Is Simpler Than It Looks

Many people assume these agent tools rely on complex state machines, task queues, or predefined execution pipelines. In fact, the core is a minimal while loop:

• Call the large language model to get the next decision.
• Execute the tool call the model selected, such as reading a file, writing a file, or running a command.
• Feed the tool results back into the model’s context.
• Call the model again until the task is complete.

There is no complex state graph and no task orchestration engine. The decision logic takes up a very small share of the total code. Most of the code is “runtime armor”: permission control, context compression, tool implementations, security protections, and other engineering safeguards.

2.2 ReAct: Reasoning and Acting in a Loop

The loop follows the ReAct (Reasoning + Acting) pattern. Each round has four steps:

1. Think: The model analyzes the task based on the current context and decides the next action.
2. Act: The model calls a tool, such as reading a file or running a command.
3. Observe: The model receives the tool’s output.
4. Iterate: The observation is added to the context, and the next round of reasoning begins, until the final answer is produced.

A key consequence follows. Each decision the model makes depends entirely on what is in the current context window. Information that is not in the context does not exist for the model.

3. Why Loss of Control Is Predictable: The Cost of Stateless Design

3.1 Why Every Session Feels Like Starting with a New Colleague

These tools are stateless by design. Each new session starts with a clean context window, so the model knows nothing about the project. It does not know the business logic behind the directory structure, the team’s naming conventions, which core files must not be modified, or which tests must pass before a commit.

Without any rules, the tool can only decide what to write next based on whatever code it happens to read. This works for small single-file tasks. In a real engineering project, loss of control is the expected result, not an occasional failure.

3.2 Turning Expectations into Constraints

The solution is straightforward. Because the model’s decisions are determined entirely by its context, the team’s standards, boundaries, and requirements should be injected into that context before the session starts. Concretely, all the rules you want the AI to follow can be written into a project-level configuration file, so the AI understands your expectations from the first message.

4. Core Practice: Building Persistent Context with Rule Files

4.1 How Rule Files Work

Most mainstream agent-based coding tools support a common convention: place a Markdown rule file with a specific name in the project root. File names differ between tools, so check the relevant documentation. The tool reads this file automatically at the start of each session and injects it into the system prompt. Its value lies in three areas:

• Persistence: Write the rules once, and they apply to every session without repeated verbal instructions.
• Team sharing: The file is committed with the codebase, so everyone on the team and their AI tools follow the same rules.
• Version control: Rule changes can be tracked and reviewed, evolving like code.

4.2 What to Put in a Rule File

A practical rule file usually covers the following categories:

# Project Guidelines
## Tech Stack and Architecture
- The backend uses a mainstream framework and follows a layered architecture: controllers do not access the database directly.
- State management uses the project's internal encapsulated module. Introducing new dependencies is prohibited.
## Prohibited Actions
- Do not modify any file under the core/ directory.
- Do not delete or bypass existing type-checking configuration.
## Code Style
- Component naming follows the project's existing conventions. Refer to existing code.
- Every new function must include unit tests.
## Workflow
- After making changes, run the project's test command and make sure all tests pass.
- Use the agreed prefix format for commit messages.

The key is to make rules specific, actionable, and verifiable. A vague requirement such as “write high-quality code” is not useful. “Run the tests after changes and make sure they pass” is a clear instruction the model can follow and check.

4.3 A Key Point: Compression and Information Loss

Agent tools generally include context compression. When a conversation grows long and approaches the context window limit, the tool automatically summarizes the earlier history to free up space and keep working.

One detail is easy to miss: rule files are read from disk again and re-injected into the context after compression, but instructions you give verbally in the chat are lost after compression.

This means any rule that must apply long-term, including coding standards, forbidden areas, and testing requirements, belongs in the rule file. A request in the chat such as “remember this from now on” is not reliable. This distinction separates occasional control from consistent control.

5. Advanced Practices: Strengthening Control

On top of rule files, you can add the following engineering measures:

5.1 Permissions and Tool Allowlists

Mature agent tools provide permission controls. You can configure which commands run automatically, which require manual confirmation, and which are blocked entirely. For write operations, such as file changes and command execution, keep manual confirmation as the default. Automate only read-only operations. This stops risks before execution.

5.2 Layered Rule Organization

For large projects, split rules by level. Put global standards in the root directory and module-specific notes in subdirectories. When the model works in a given directory, it reads the nearest rules, so rules apply where they are relevant. This also prevents one large file from burying the critical constraints.

5.3 Task Decomposition and Pre-Commit Checks

Long tasks tend to drift as the loop repeats. Break large requirements into small tasks and drive them one at a time. After each task, run tests and code checks. In the rule file, specify that the agent should report progress after finishing each subtask before continuing. This creates checkpoints for human review.

5.4 Supporting Cloud Development Environments

In team settings, rule files can be connected to code hosting and CI pipelines. Rules then become part of the codebase and are enforced as such. Combined with logging and monitoring, every step of AI-assisted development can be recorded and audited. Mainstream cloud platforms’ development toolchains generally offer capabilities of this kind, which can serve as supporting infrastructure for engineering adoption.

6. Conclusion

An AI coding assistant is not a black box where you send instructions and receive code. It is a ReAct system driven by a minimal loop, and its decisions depend entirely on context. Its loss of control is not a capability problem. It results from missing context. Once this is clear, the solution is clear too:

1. Write team standards, forbidden areas, and workflows into rule files, so the AI works within these constraints from the first loop iteration.
2. Use the fact that rule files are re-injected after compression to make important rules persistent, rather than relying on verbal instructions.
3. Add permission controls, layered rules, and task decomposition to build a complete, controllable development loop.

With these practices, AI coding assistants can move from “occasionally impressive, often unreliable” to productive tools that are stable, controllable, and suitable for team collaboration.