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What AGENTS.md optimization can—and cannot—do
AGENTS.md is an instruction-file format, not a guarantee about runtime behavior. Tools differ in which instruction files they recognize, how they combine them, and whether instructions reach subagents. Even documented support can be specific to a product or mode: check the current documentation for the exact harness you use.
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Optimization therefore means making repository guidance maintainable and usable in its intended context—not merely shortening it or adding more rules. The goal is to catch defects such as stale commands, unclear scope, and conflicting guidance, then confirm actual behavior in the target tool. No validated cross-tool maximum length, token budget, or universal lint specification has been established.
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First establish which instructions each tool loads
Before editing shared guidance, inventory the tools, versions, modes, and agent types your team actually uses. Record which files each loads and whether a primary agent and its subagents receive the same repository context. Do not assume that support documented for one product applies to every product from the same vendor.
| Tool and documented context | What the documentation says | Practical implication |
|---|---|---|
| VS Code | Microsoft lists AGENTS.md among local instruction choices, alongside .github/copilot-instructions.md and CLAUDE.md. It also documents pattern-scoped .instructions.md files and cautions that discovery and merging vary by harness. Microsoft’s custom-instructions documentation | Use the mechanism and scope supported by the specific VS Code workflow. Do not depend on an assumed universal file order or precedence rule. |
| GitHub Copilot CLI | GitHub says built-in explore, task, and code-review subagents do not receive repository instruction files by default. A custom subagent can receive them when configured with include-custom-instructions: true. GitHub Copilot CLI command reference | Test subagent behavior separately; repository instructions reaching the main CLI agent does not mean built-in subagents inherit them. |
| Cursor CLI | Cursor’s CLI documentation says it reads root AGENTS.md and CLAUDE.md alongside .cursor/rules. Cursor also documents its own rules mechanism. Cursor CLI documentation and Cursor rules documentation | Confirm support for the exact Cursor product and mode in use, and choose scope accordingly. |
These documented examples are not a complete compatibility matrix. Record your own setup rather than inferring behavior for another CLI, editor, agent type, or version.
Choose instruction scope deliberately
Put a rule where it will govern the intended work and where the target harness can discover it. A root-level file can suit repository-wide conventions when the tool supports it; nested files may be appropriate for directory-specific work; pattern-based instructions can target selected files when the harness supports that mechanism.
Rank #2
Compare instruction arrangements on the dimensions that affect your repository:
- Harness support: Does the exact tool and mode recognize the file or instruction mechanism?
- Scope: Is the guidance repository-wide, directory-specific, or limited to matching files?
- Merge and precedence: Does the tool document how multiple instruction sources interact, or is the behavior uncertain?
- Subagent access: Does the relevant subagent receive the instructions, and is additional configuration needed?
- Maintenance cost: Will separate tool-specific files duplicate rules and drift apart?
Keep broadly applicable repository rules in an appropriately scoped shared file where the target tools support it. Use file-specific or tool-specific mechanisms for guidance that does not belong everywhere. Where precedence is undocumented or varies, avoid relying on a particular ordering to resolve conflicting instructions.
Rank #3
Build a practical AGENTS.md lint checklist
The following checks are useful engineering recommendations based on documented differences in discovery and scope. They are not a vendor-mandated or empirically validated universal lint standard.
- Paths and commands: Do referenced files, directories, scripts, and commands exist and still work in the repository?
- Tool references: Does the file name tools that the intended environment no longer uses or cannot access?
- Scope: Can a reader tell which code, workflow, or agent a rule governs? Is a repository-wide rule unnecessarily broad?
- Conflicts and duplication: Do rules contradict one another or repeat across files in ways that are likely to drift?
- Freshness: Are version-specific details, links, and procedural references still current?
- Actionability: Does each rule tell the agent what to do, when it applies, and how to verify the result where that matters?
Do not impose a word-count or token ceiling as if it were a proven quality threshold. The available evidence does not establish a validated maximum length or a tool-independent set of lint rules.
Verify instruction discovery and behavior in the real harness
- Choose a representative task. Pick a small repository change that exercises a few important constraints in the instruction file.
- Run it in every target configuration. Test each relevant tool, mode, and agent type, including subagents when your workflow relies on them.
- Check discovery explicitly. Inspect the tool’s available context or execution output where possible to establish whether the intended instruction file was loaded. Do not infer discovery solely from a plausible result.
- Check adherence against observable requirements. Confirm whether the agent followed the selected constraints and completed the expected verification steps.
- Revise and retest. If an instruction was missed, distinguish a discovery or scope problem from unclear, conflicting, or stale wording before changing the file.
This is a practical validation approach, not a protocol prescribed by the cited vendors. Repeat the checks after tool upgrades or configuration changes because support and harness behavior can change.
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A 2026 paper, Evaluating AGENTS.md: Are Repository-Level Context Files Helpful for Coding Agents?, reports that in its evaluated settings context files encouraged broader exploration and agents tended to respect the instructions. Those observations describe the study’s settings; they do not establish that AGENTS.md always improves correctness or causes better outcomes in every repository or tool. Read the paper on arXiv.
Best Value
A separate 2026 paper, On the Impact of AGENTS.md Files on the Efficiency of AI Coding Agents, describes a study covering 10 repositories and 124 pull requests. That is the study’s scope, not a reported efficiency gain. Read the paper on arXiv.
Together, these studies are reason to evaluate context files carefully—not to promise universal improvements in agent correctness, speed, or token use.
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