How to Write High-Quality Instruction Files for AI Coding Agents

1. Introduction: Why Instruction Files Determine an Agent’s Reliability

AI coding agents are now commonly used by development teams. They can understand natural language requirements, read codebases, execute builds and tests, and autonomously make changes across multiple files. However, one often overlooked fact is this: the same agent can perform very differently in engineering work depending on how its instruction file is configured.

When many people write an agent instruction file for the first time, they tend to treat it as a README written for an AI: a few sentences about the project and that is it. The result is often predictable. The agent may generate code that does not match the project’s conventions, modify directories it should not touch, or forget to update related files after changing code. The root cause is usually not the agent itself, but the fact that the instruction file does not clearly state the key constraints the agent needs to know.

This article takes a neutral approach and abstracts a general design method for instruction files used by AI coding agents. The goal is to help you write a configuration that genuinely improves reliability on any agent that supports instruction files.

2. What Is an Agent Instruction File?

An agent instruction file is an instruction-oriented file designed for AI coding agents. It is usually written in plain Markdown, has no mandatory required fields, and can generally be placed in the project root directory to be automatically recognized. Its core purpose is to inject project context, standards, and constraints into the agent before it starts working.

The key to understanding it is to distinguish it from several related concepts.

Difference from a README

A README is written for humans. Its goal is to help developers quickly understand what the project does, how to run it, and how to contribute. An instruction file is written for an agent. Its goal is to tell the AI what build and test commands to use, which code style requirements are mandatory, which directories are generated artifacts or restricted areas, and which files need to be updated after code changes.

In other words, a README focuses on understanding, while an instruction file focuses on execution.

Difference between private and open instruction specifications

Early instruction files were often private configurations specific to a particular tool, recognized only by a specific agent. As the industry has matured, open instruction specifications maintained by the community have appeared and have been adopted by more and more coding tools. The value of such an open standard is that one file can be reused by multiple agents. Teams do not need to maintain a separate instruction set for every tool, which reduces maintenance cost and the risk of inconsistency.

For teams that use multiple tools at the same time, a common recommended practice is to import the content of the open instruction file into a private instruction file using an import or reference statement, so that one specification can take effect in multiple places.

3. Loading Mechanism: Where Instruction Files Live and How They Take Effect

Before writing a good instruction file, you need to understand how the agent loads it. Many agents construct an instruction chain when they start up. This chain organizes instructions at different levels according to priority. The process usually runs only once at startup, so the instruction file should be ready before the agent starts.

Loading generally follows a hierarchical structure from global to local.

• Global or user-level instructions. The agent first looks for a global instruction file in a configuration directory under the user’s home directory. This level is used for cross-project preferences, such as general response style, security boundaries, or personal habits.
• Project-level instructions. The agent then looks for a project-specific instruction file in the project root directory. This level describes the current repository’s build process, directory structure, and team conventions.

A common design pattern is override priority. When both a normal instruction file and an override instruction file exist, the agent usually uses only one of them rather than simply merging them together. This allows local adjustments without modifying the base file. In addition, some agents support placing instruction files in subdirectories so that rules for specific modules can take effect locally. This is especially useful in large monorepos.

A practical conclusion follows from this mechanism: put team-wide general conventions in the global layer, and put project-specific build and directory constraints in the project layer. Avoid mixing the two types of content, which can cause conflicting instructions or make maintenance difficult.

4. What a High-Quality Instruction File Should Include

There is no fixed template for an instruction file. However, if the goal is to help the agent correctly read code, modify code, and verify code, the following categories of information are almost always necessary.

1. Build, test, and common commands

This is often the highest-value type of information. Clearly stating commands for dependency installation, compilation, running, unit testing, and linting allows the agent to verify its changes autonomously instead of guessing.

## Common Commands
- Install dependencies: `npm install`
- Run locally: `npm run dev`
- Run unit tests: `npm test`
- Run linting: `npm run lint`
- Before committing, make sure both commands pass: `npm run lint && npm test`

2. Code style and engineering standards

This includes naming conventions, indentation, import order, comment requirements, whether strong typing is required, and error-handling patterns. These constraints can significantly reduce style drift in agent-generated code and reduce rework later.

3. Directory and file constraints

Clearly define which directories are automatically generated and must not be edited manually, which areas are sensitive, such as infrastructure configuration or migration scripts, and where new files should be placed. This is critical for preventing the agent from doing something well-intentioned but harmful.

4. Change synchronization requirements

Tell the agent which documents or type definitions must be updated after an interface is modified, which migration files must be updated after a database schema changes, and which test cases must be added after a new feature is introduced. This point is often overlooked, but it is central to keeping the codebase consistent over the long term.

5. Workflow preferences

For example, whether the agent is allowed to commit directly, whether it should first output a plan before executing, and whether it should ask questions or propose a solution first when facing ambiguous requirements.

5. Best Practices for Writing and Improving Reliability

• Be specific rather than abstract. Replace vague statements such as “Please ensure code quality” with executable requirements such as “Before committing, run command X and make sure it passes.” Agents follow executable instructions more reliably.
• Keep it concise and focus on constraints. An instruction file is not a place to pile up documentation. Long explanations can dilute attention from critical constraints. Keep only the information the agent needs to know every time.
• Maintain instructions in layers. Put stable general conventions in the global layer, put evolving project-specific rules in the project layer, and use subdirectory-level instructions for local exceptions.
• Align with automation. Keep the commands in the instruction file consistent with the commands used in CI/CD pipelines. Only then can the agent’s local verification align with production results.
• Iterate continuously. Whenever you notice the agent repeatedly making the same type of mistake, add the corresponding constraint to the instruction file. It should be treated as a living document that evolves with team collaboration, not a one-time configuration.

6. Conclusion

The reliability of AI coding agents largely depends on whether we clearly express the project conventions that humans assume by default but AI does not know. A high-quality instruction file is essentially a shared, executable working contract between humans and machines.

Rather than focusing only on which agent to choose, it is more valuable to make instruction files solid as a piece of infrastructure. Regardless of the tool used, clear build commands, explicit directory constraints, and strict change synchronization rules are the common prerequisites for enabling an agent to consistently produce high-quality code.