Rules Configuration and System Prompts for IDE Agents

Here's the difference in plain terms. A chat assistant that gives a bad answer gets corrected on the next turn. An agent that misreads what you meant will act on that misreading across dozens of steps before a human ever sees the result. By the time you notice, it's already touched a dozen files.

An IDE agent doesn't just answer questions. It reads files, calls tools, runs shell commands, watches what happens, and decides what to do next, on its own, for as many steps as the task takes. That loop, plan, act, observe, re-plan, is what makes it an agent instead of a chatbot.

The agent isn't broken when this happens. It's unconstrained. Rules files and system prompts are the mechanism that turns "what the developer meant" into something the agent can actually act on, instead of guessing. Get that mechanism right, and the agent stays on task. Get it wrong, or skip it, and you're debugging a wandering agent instead of shipping code.

How system prompts and rules files are read and assembled at runtime

A system prompt is the operating context the agent loads before it does anything at all, the ground rules it carries into every decision.

Rules files are how project-specific instructions get folded into that context. Two formats show up constantly here, and they behave differently in ways worth knowing:

• CLAUDE.md gets re-read straight from disk at the start of every session, and again after every context compaction event. In a long-running task, the conversation history gets compressed to save space, but CLAUDE.md gets reloaded fresh, so its rules survive compaction in a way that regular conversation doesn't.
• AGENTS.md sits at the repo root and gets read natively by most major agents without any special setup.

Claude Code also walks the directory tree, picking up every CLAUDE.md file it finds along the way and layering them together. Rules from a parent folder sit underneath rules from the project-level file, so a monorepo can carry shared standards at the root and let each service refine them closer to the code.

Modern agent system prompts aren't one fixed block of text. They get built on the fly, depending on which tools are active, which mode the agent is running in, and which rules files exist in the project. A rules file isn't documentation someone might read someday. It's a live input that shapes every single decision the agent makes. How it's written and where it's scoped decides what actually gets weighed when the agent picks its next move.

The current landscape of config file formats and where each one lives

There are five layers of config in common use right now: always-on instructions, on-demand skill files, MCP server configs, editor-specific rule files, and repo-level context files. Here's where the major ones live.

CLAUDE.md is a Markdown file that works as a persistent, project-scoped system prompt. Anthropic recommends keeping it under roughly two hundred lines, and files past a certain size get skipped entirely rather than partially read. Claude Code also keeps its own per-project notes automatically, loaded into every session by default (this can be turned off with an environment variable). For monorepos, a setting called claudeMdExcludes lets one team opt out of another team's rules file, so nobody's agent is following instructions meant for a different part of the codebase. This directory-based setup replaced the older single .cursorrules file, and it enables selective loading: the agent only pulls in what's relevant instead of every rule for every request, spending fewer tokens for more signal.

AGENTS.md is a plain Markdown file at the repo root, and it's read natively by a wide range of agents, including Codex, Copilot, Windsurf, and Aider. It's stewarded by the Agentic AI Foundation and already shows up in a large number of public repos. GitHub's coding agent added support for it in August 2025, and also reads CLAUDE.md and GEMINI.md, which tells you something about where the industry is converging.

GitHub Copilot has its own layered setup: repo-wide rules in .github/copilot-instructions.md (available since 2024), scoped instructions in .github/instructions/*.instructions.md that use glob patterns to target specific files (added mid-2025), and org-level instructions that reached general availability in early 2026 for teams running Copilot across many repos at once.

GEMINI.md is Google's version of the same idea: a project memory file that gives Gemini agents persistent context, working much like CLAUDE.md does.

None of this is a single correct standard. It's a menu. The right format depends entirely on which agents a team is actually running.

The portability problem when a team runs more than one agent

Most developers working at the edge of this stuff use more than one AI coding tool: an AI IDE for daily editing, a terminal agent for bigger multi-file jobs. That's normal. What's not sustainable is maintaining a separate rules file, written independently, for each one.

Here's the tax that creates. Coding standards change, say, the team switches error-handling patterns, and now that update has to be made by hand in every format, for every tool, separately. Miss one, and the files drift apart. Different agents end up operating under different constraints on the same codebase, which defeats the entire purpose of writing rules in the first place.

The fix depends on the team's setup:

• Running one tool? Use its native format. Don't build an abstraction layer for a problem that doesn't exist yet.
• Running multiple tools? Pick AGENTS.md as the single source of truth, and only add tool-specific files for things that one tool needs that the others don't.
• Alternative approach: keep CLAUDE.md as the canonical file, and treat AGENTS.md and any editor-specific files as lightweight references pulled from it. Either way works, as long as there's one clear source and a rule against editing the copies independently.

The real question underneath all of this isn't "which file format." It's "who owns keeping this current, and which format makes that job easiest to actually enforce."

What an effective rules file actually contains and how to structure it

A rules file that works tends to have five things in it, doing five different jobs.

Identity and role framing. Who is the agent, what project is it in, what kind of calls is it expected to make. This sets the frame before any task even starts.

Behavioral rules. How to approach a task, what needs a human's okay before proceeding, what the output should look like. Think of this as the agent's operating constitution.

Coding standards. Naming conventions, test coverage expectations, which dependencies are allowed. Basically, everything a senior engineer would flag in code review, written down ahead of time so the agent isn't left guessing.

Tool usage instructions. Not just a list of available tools, but when to reach for one over another, what side effects to expect, and what to check before and after using it.

Safety and reversibility constraints. These work best woven through the whole document, not tacked on as one section at the bottom. Safety that's structural beats safety that's a footnote.

A few structural notes matter here too. Headings and bullet points aren't just for readability, they create a hierarchy the agent uses to decide what to prioritize. Explicit checkpoints ("verify the migration ran before touching the next table") cut down on skipped steps and reduce the odds of the agent inventing a result it never actually checked.

Length is a real constraint, not a nice-to-have. A rules file that tries to cover every possible scenario turns into noise, because the agent can't tell what actually matters when everything is written with equal weight. A good test: if a rule isn't changing behavior in some noticeable way, cut it. It's padding.

Splitting a big rules file into smaller, scoped ones (one for TypeScript conventions, one for database migrations) tends to beat one giant file. The agent only loads what's relevant to the task in front of it, which means fewer tokens spent and a better signal-to-noise ratio on the instructions that matter.

How scoping and rule types change what the agent loads and when

Not every rule needs to be loaded all the time. Loading everything on every request is its own kind of failure: it burns tokens and buries the instructions that actually apply to the current task under a pile of ones that don't.

A few common scoping patterns:

• Agent-requested: the agent pulls a rule in when it decides it's relevant. Good for specialized workflows that only come up sometimes.
• Glob-scoped: a rule attaches only to files matching a pattern. Migration rules load only when the agent touches files in a migrations folder, and stay out of the way otherwise.
• Manual @-mention: the developer brings a rule in by name. Good for edge cases, or rules used rarely enough that auto-loading them every time would just be waste.

The design principle underneath all of it: match the scope of the rule to the scope of the situation it covers. A rule about handling schema changes doesn't belong sitting in the always-on global context, cluttering every request. A rule about the project's general error-handling philosophy probably does.

CLAUDE.md's tree-walking behavior fits neatly here too. Shared standards live in the root file, and rules specific to one service sit closer to the code they govern, in a subdirectory file. The job of writing rules isn't just about getting the wording right. It's also about choosing the right place to put each one.

The prompt engineering principles that apply specifically to agent rules

A weaker model with a tightly written prompt will consistently beat a stronger model working off vague instructions. The prompt is doing most of the actual work.

Be specific, not aspirational. "Write clean code" tells the agent nothing, because it already believes it's writing clean code. "Use named constants instead of magic numbers, and break up any function over twenty lines into named helpers" actually tells it something.

State what to do, not just what to avoid. Agents follow "do X" more reliably than "never do Y." A prohibition forces the agent to reason about the whole space of things it shouldn't do, which opens the door to ambiguity. Where a prohibition really is necessary, pair it with the positive version: don't just say "never push to main," say "open a pull request instead."

Spell out reversibility, explicitly. Before deleting files, dropping a database table, or pushing to main, the agent should stop and confirm. This is the actual safety net for long autonomous runs, not faith that the model will "know better," but a tripwire written directly into the rules.

Use conditional logic for high-risk areas. Something like: "if the task touches the auth layer, stop and summarize the proposed changes before making them." This is how a developer dials the agent's independence up or down depending on how much is at stake in a given part of the codebase. Call it an autonomy slider: rules aren't a simple on/off switch, they're a graduated set of limits that define how much room the agent has to operate on its own, in this project, right now.

Rules deserve the same treatment as code. Change a rule, run a real task, check whether the behavior actually shifted the way intended. Rules are living configuration, not a setup step to check off once.

Where rules configuration falls short and what fills the gap

Rules files shape intent and style. They don't control the execution environment, they don't set tool permissions, and they can't reach into what the agent is physically able to touch. A well-written rules file also can't rescue a badly written task. Rules set the boundaries the agent operates inside; the task itself still has to spell out what success actually looks like.

There's a hard ceiling here too: the context window. A rules file that grows too large either gets cut off or gets diluted, meaning the agent technically reads it, but later instructions carry less weight than earlier ones simply because of where they sit in the file. That's the real reason length discipline and splitting rules into scoped files matters. It directly decides whether the agent follows the rules at all.

Rules also go stale. A file written six months ago might describe a project that's since changed shape entirely, and stale rules don't just stop helping, they actively point the agent in the wrong direction.

None of this means rules files are the wrong tool. It means they're one layer among several, and the gaps get filled by other things:

• MCP servers, for live external context a static file can't capture
• Skill files, for specialized knowledge that only needs to load sometimes
• Human review checkpoints, written directly into the rules as conditional stops
• Auto-memory and agent-maintained notes, working alongside the static rules file as the agent's own running record of the project

Building rules configuration as a team practice rather than an individual habit

A rules file checked into a repo isn't a personal preference file. It's shared infrastructure, and it shapes how every developer's agent behaves on that project, whether they wrote a line of it or not.

That means it deserves the same handling as any other piece of shared configuration:

• Changes to the rules file go through pull request review, the same as changes to a build script or a CI config. No quietly editing it and moving on.
• The reasoning behind a rule gets written down somewhere, so six months later nobody's left guessing why a rule exists, or whether it's safe to remove.
• Someone owns keeping it current, the same way someone owns the CI pipeline.

Who's responsible for these rules, and what does the team need in place so falling behind doesn't happen quietly, is the same portability question from earlier, seen from a different angle. An agentic development environment like Cursor works as an autonomous agent, reading files, calling tools, running shell commands, and chaining actions across many steps based on what it observes along the way. That autonomy is precisely why intent has to live in explicit, written rules instead of sitting in someone's head. A rules file is the steering wheel, not a chore to get through once at project setup.