Layering AI Instructions: Project vs. Personal Rules

The Problem of Layered AI Instructions

In collaborative software development, AI agents are increasingly used to assist with tasks ranging from code generation to enforcing coding standards. Project teams often maintain a central repository of instructions, such as an agents.md file, which outlines desired practices like architectural patterns or preferred libraries. This file serves as a shared source of truth for the entire team's AI interactions, aiming for consistency and adherence to project goals.

However, individual developers frequently encounter unique circumstances. These can include specific workflow preferences, environment-specific constraints like local path configurations, or the need for hardware-specific optimizations that don't apply universally. When an AI agent needs to reconcile a project-wide instruction (e.g., 'Use Library A') with a developer's immediate requirement (e.g., 'Use Library B for local testing'), a mechanism for prioritizing local rules is essential. The core challenge is enabling this granular customization without requiring developers to manually alter shared, tracked files, thereby risking accidental commits of their personal configurations.

Existing Approaches and Potential Solutions

One proposed architectural pattern involves introducing a parallel file for local overrides. For instance, a system might look for an AGENTS.local.md file within a specific submodule's directory. If present, the instructions within this local file would take precedence over those in the main AGENTS.md. This approach is conceptually similar to how tools like Git use .gitignore files to selectively exclude certain files or patterns from version control, offering a familiar paradigm for developers.

The success of this method heavily relies on the specific AI agents and their ability to process and prioritize multiple instruction sources. If agents are not designed to automatically detect or parse these secondary configuration files, a more active merging strategy might be necessary. This would involve a pre-processing script that combines the content of the global AGENTS.md with any available local override files into a single, unified instruction set that the AI agent then consumes.

The 'Steering File' Concept

A more robust and flexible approach, especially given the rapidly evolving landscape of artificial-intelligence tools, is the adoption of a 'steering file' concept. Instead of relying on specific naming conventions that might vary between AI frameworks, the emphasis is on the functionality: a file that the AI agent automatically loads and prioritizes in its context for every operation. This could be a file like .kiro/steering/personal_preferences.md in a project directory, or even a file stored in a user's home directory, such as ~/.kiro/steering/.

The key characteristic of such a steering file is that the AI agent is configured to treat it as an immutable layer of personal guidance. When the agent processes a prompt, it would inherently include the directives from this steering file, ensuring that personal preferences—whether they are debugging configurations, alternative library choices, or specific output formats—are consistently applied. This allows developers to maintain their individual operational needs without manual intervention or modification of shared project resources.

Implementing Layered Precedence

To achieve true layering, the AI agent's prompt engineering or internal logic must be designed to handle hierarchical instructions. When synthesizing a prompt, the system would first consult the global configuration (e.g., agents.md) and then layer on any applicable personal configurations. If a rule exists in both layers, the personal rule must explicitly override the global one. For example, a personal rule could be phrased as, 'When encountering the instruction to use Library A from the global configuration, override it with Library B for this session.'

The actual mechanism for applying this precedence can vary. Some agent frameworks might support distinct configuration slots for global and personal settings. In other cases, a utility script might be responsible for reading both sources, resolving conflicts based on defined precedence rules, and then presenting a single, consolidated instruction set to the AI model. The goal is to abstract this complexity away from the end-user developer, ensuring that their personalized settings are seamlessly integrated into the AI's decision-making process.

Agent-Based Systems and Customization

Agent-based systems are particularly well-suited for this layered instruction architecture. By defining agents as entities with distinct roles and configurable behaviors, it becomes natural to provide them with multiple sources of directives. A 'code generation agent,' for instance, might be instructed by agents.md to follow project-wide linting rules, while a personal steering file might tell it to enable more verbose logging for debugging purposes during a specific development sprint.

This level of customization is crucial for fostering developer autonomy within a structured environment. It allows for exploration of alternative approaches or optimizations on a local level without disrupting the team's established standards. The artificial-intelligence agent acts as an intermediary, capable of understanding and applying both the broad organizational directives and the specific, individual requirements, thereby enhancing productivity and flexibility.

Where This Approach Does Not Fit

This layered instruction architecture is most effective when dealing with explicit, text-based instructions that AI agents can parse and interpret. It may not be suitable for highly sensitive security configurations or complex, stateful environmental setups that require more than just declarative rules. If an AI agent's core functionality is not designed to accept or prioritize external configuration files or context, retrofitting this layering mechanism could be difficult and may require significant modifications to the agent itself.

Furthermore, the success of this approach depends on the AI model's ability to understand and adhere to nuanced instructions. If the underlying artificial-intelligence is prone to hallucinating or misinterpreting directives, even layered rules might not guarantee consistent behavior. Teams should also consider the overhead of managing multiple configuration files, ensuring that developers understand where to place their personal rules and how they interact with project-wide standards to avoid confusion.

This article draws on Architecture for layering project-wide AI instructions with user-specific "personal" rules and answers to it (1, 2), on the Stack Exchange network, licensed under CC BY-SA 4.0.