Core Principles
These principles explain why conciseness and clarity are essential for high-performing agents.
- Token Cost: Verbose explanations multiply token usage across many interactions
- Signal-to-Noise Ratio:
- Excessive detail dilutes critical information
- Agents may struggle to identify which rules are most important
- Too many examples can distract from the core pattern
- Processing Overhead:
- Longer documents = more cognitive load
- Simple, direct imperatives are faster to process than lengthy explanations
Actionable Best Practices
- Be Concise: Eliminate verbose statements and any language or content that is only relevant to human readers.
- Focus on New Learning: Only insert rules that correct known AI mistakes or introduce "new" learning. Rules the AI can already generate on its own are typically noise.
- Avoid Redundancy: If a rule or principle is stated clearly once, do not repeat it.
- Use Minimal, High-Quality Examples: One good, representative example is more effective than three similar ones.
- Prioritize High-Level Abstraction: Focus on stable, high-level principles over low-level, volatile details like method names.
The Maintenance Burden
- Focus on Backbone Principles: Guide the AI with high-level, enduring principles rather than specific, volatile details. Code evolves quickly, and outdated specific guidance becomes a maintenance burden and can actively mislead the AI.
- Team Maintainability: Long, verbose AGENTS.md files create maintenance problems:
- Developers overwrite each other's rules without noticing
- Hard to tell if changes improve or degrade agent performance
- Outdated specifics mislead the agent
Conclusion
A well-crafted AGENTS.md is a powerful tool for developing highly effective AI agents. If you let AI generate the AGENTS.md, try creating AGENTS.md using the following rule:
- Make AGENTS.md concise:
- Remove human-only instructions
- Eliminate redundant or duplicate rules
