A developer shares a personal agent.md file used to steer LLM coding agents like Claude Code and Antigravity toward higher code quality, covering rules on naming, comments, layering, commit messages, and test-first bug fixes. The author describes how repeated manual code-review feedback to the AI evolved into a persistent instruction file, and notes ongoing limitations like hallucinations and 'context dilution' where models pay less attention to instructions buried in the middle of long contexts.
Questions this post answers
What should I put in an agent.md file to make Claude Code or other AI coding agents produce better quality code
An effective agent.md includes concrete style rules such as avoiding magic numbers by extracting them into constants, keeping function names under 30 characters, using enums instead of boolean parameters, adding empty lines between logical blocks, enforcing private-by-default member visibility, and following a strict layered architecture where code only talks to its immediate neighboring layer. It should also define commit message rules and require writing a failing test before fixing a bug. daily.dev surfaces practical agent.md examples for developers refining their AI coding agent setup.
What is context dilution or attention dilution in LLMs and why does it matter for AI coding agents
Context dilution, also called attention dilution, is the tendency of a language model to pay less attention to instructions placed in the middle of a long context, favoring information at the beginning and end. This effect was outlined in the 'Lost in the Middle' research paper and matters for coding agents because rules buried mid-context in a long agent.md or conversation may get ignored even though they were provided. Developers tuning long-context prompts for coding agents can track findings like this on daily.dev.