Providing your AI coding assistant with an 'AGENTS.md' file containing clear workflow guidelines and rules skyrockets the task success rate from 30% to 90%.
Imagine this. A brilliant genius intern who graduated at the top of their class from Harvard University has just joined your team. You casually instruct this smart intern, “Please tweak the main screen of our company website a bit.” As soon as the intern receives the instruction, they stay up all night pulling out all the latest technologies to create a flashy, astonishing web page.
However, when you actually upload it to the server, a huge problem arises. Our company traditionally needs to maintain a calm blue brand color, but the intern changed the design to a blindingly bright red. Worst of all, they arbitrarily used cutting-edge methods that don’t integrate at all with the old internal database system we’ve been using for 10 years. In the end, not a single line of the code the intern passionately wrote could be used.
Did this smart intern make these mistakes because they are foolish? No. This intern was technically perfect, but completely unaware of our company’s unique ‘workflow rules’ and ‘background context’. No matter how brilliant a talent is, if they don’t receive a proper handover of tasks, they have no choice but to stumble.
In the world of software development today, this exact same thing happens every minute of every day. Following the advent of ChatGPT, AI coding agents (artificial intelligence that understands human language and autonomously writes code and performs software development tasks on behalf of humans) are widely used. However, if developers blindly assign tasks to these smart AIs, they often end up breaking existing systems or producing entirely wrong results, just like the genius intern earlier. This is because while AI knows vast general knowledge from the internet, it doesn’t know the specific internal rules unique to ‘our company’s project.’
To solve this chronic problem, there is a magic key that has recently become an essential standard among IT companies worldwide. It is the ‘AGENTS.md’ file, a dedicated handover document and workflow guideline for AI. From now on, I will easily explain how this single small document can transform your AI assistant into a genius powerhouse.
Why It Matters
You might think, “I’m not a programmer and I don’t know the first thing about coding, so do I really need to know this?” However, this story isn’t just a complex technical tale for people who code computers.
Soon, we will all be working with smart AI assistants inside our computers. An AI assistant that automatically organizes Excel data, an AI assistant that summarizes piled-up emails every morning and writes replies for you, an AI assistant that helps design PowerPoint presentations—though the roles and fields vary, the era of collaborating closely with artificial intelligence is already wide open before our eyes. At this time, the method of making AI work accurately and politely according to your intentions, that is, ‘the method of properly explaining the unique rules of your work to AI,’ will become an essential survival skill that every office worker must equip themselves with in the future.
The power of clear workflow instructions is starkly proven by objective numbers. If you look at research related to Context Engineering (the technology of effectively conveying context and background knowledge to AI) published in 2025, there is a very shocking experiment result. AI agents that were instructed to perform complex coding tasks independently, without a guideline file informing them of the specific project’s background context, had a probability of successfully completing the task correctly of only around 30%. They wrote wrong code or made fatal errors conflicting with existing systems 7 out of 10 times.
Surprisingly, however, when well-written context files were placed in the project folder in advance and the same task was instructed, the AI’s task success rate skyrocketed to a whopping 90% [Context Engineering 2026: AGENTS.md, CLAUDE.md, and .cursorrules That …]. By simply adding one text file, the AI’s work capability jumped three times. This result implies that no matter how expensive and outstanding the latest AI model you pay for monthly is, it’s virtually a ticking time bomb waiting to go off without the backing of proper guidelines. Simply put, it’s like hiring the world’s best carpenter and asking them to build a house without a blueprint.
The Explainer
Then what exactly is ‘AGENTS.md’?
In the software development field worldwide, there has long been a beautiful practice of placing a ‘README’ file on the very first screen of a project folder. When a new fellow developer joins the team, this file serves as a comprehensive guide that kindly explains in ‘human language,’ “This program was created for this purpose, here is how to install it on your computer, and this is how to use it.”
However, the README file was written purely to aid human understanding. It doesn’t explain what mechanical rules the AI must follow when writing code in this project. AGENTS.md was born to perfectly fill this exact gap. This file is a simple, open-format document specifically designed to kindly guide AI coding agents, not humans. Simply put, it’s accurate to think of it as a “customized workflow instruction manual for AI assistants” [GitHub - agentsmd/agents.md: AGENTS.md — a simple, open format for guiding coding agents], [AGENTS.md], [AGENTS.md & SKILL.md: The Complete Guide (2026)].
The method of using this file is unbelievably simple. All you have to do is create a Markdown file (a lightweight document format that applies simple formatting to text without complex code) with a .md extension in the top-level folder (root repository) where the project’s files are gathered.
Then, before starting the actual work, the AI coding agent that received the user’s command will scan this document first and absorb the rules like a sponge. The most interesting part is that this document is firmly positioned right after the system prompt (the absolute top-level command that sets the AI’s basic persona and code of conduct, initializing its brain structure) [A Complete Guide To AGENTS.md]. Through this, even before starting the work, the AI becomes a reliable worker perfectly acquainted with the ecosystem and rules of this project.
If the existing README document for humans emotionally explains “What this project ultimately does,” AGENTS.md is a dedicated space that objectively and precisely informs “How the AI should specifically work in this project” [AGENTS.md & SKILL.md: The Complete Guide (2026)].
To use an analogy, it’s like this. You hired a 3-star Michelin chef (the latest high-performance AI) with the world’s best cooking skills for a huge salary and put them in charge of your restaurant’s kitchen. Without any guidelines, the chef will casually make complex authentic French cuisine heavily loaded with expensive caviar and truffles. The dish itself might be artistically superb, but if our restaurant is a Korean diner that sells quick and cheap 10-dollar Kimchi stew for exhausted office workers, this dish is completely useless. Customers will vent their frustration, and the restaurant will soon close down.
What if you stuck clear operating rules (AGENTS.md) like these largely on the kitchen wall at this time? “1. Our restaurant is a Korean diner specializing in stews. 2. All dishes must be served to the customer within 15 minutes of ordering. 3. Spiciness is fixed at level 3. 4. High-end ingredients costing over 5 dollars are absolutely prohibited.”
Only then will the chef fully utilize their excellent cooking skills to boil the fastest Kimchi stew with the deepest taste in the world, matching the set budget and speed. It’s the moment when the once-uncontrolled top-tier intelligence is finally tamed perfectly within ‘our restaurant’s rules’ and truly shines.
Where We Stand
| This surprising yet simple methodology is rapidly becoming a new, powerful standard in the global IT industry, including Silicon Valley. In just the past year, quietly adding a guideline file like AGENTS.md or CLAUDE.md (a file dedicated to a specific AI model) to a coding repository has become the most basic practice. The agent developers currently leading the global AI market, such as Anthropic, OpenAI, and Qwen, unanimously and highly recommend that all users use this method [[DoAGENTS.md/CLAUDE.mdFilesHelpCodingAgents? | Towards AI](https://towardsai.net/p/machine-learning/do-agents-md-claude-md-files-help-coding-agents-a-new-paper-challenges-this)]. Furthermore, overview analysis documents on how to effectively write these files for various AI coding assistants are pouring out daily [Instruction Files for AI Coding Assistants: An Overview]. |
However, there is a very core fact that we must all pay attention to here. Merely imitating this and roughly creating an empty text file absolutely does not make all problems magically disappear.
Let’s look at the interesting analysis results posted on the GitHub blog, the world’s largest source code hosting platform. As a result of closely analyzing cases from over 2,500 different repositories, it was found that most agent instruction files ambitiously created by people failed completely. The reason was that they were too ‘vague’. For example, simply writing vague things in the file like “You are a helpful coding assistant” or “Write the code neatly and beautifully” doesn’t provide even a speck of help to the AI, which is a machine.
A great AGENTS.md file that brings out 100% of the AI’s capabilities must be fiercely specific. It must meticulously include the persona the AI should adopt, the exact tech stack used in the project, the folder structure where project files are stored, workflows, explicitly executable terminal commands, and code style examples. And the most important thing is setting strict and clear boundaries on ‘what it must absolutely never do’ [How to write a great agents.md: Lessons from over 2,500 repositories - The GitHub Blog].
For example, instead of a simple “Make it well,” you must engrave very specific and unconditional rules into the document, such as “When writing a new feature, do not arbitrarily download and add a new library from the internet; you must reuse what is in the existing folder,” or “Before merging code so other developers can review it, be sure to run the syntax error checking command npm run lint yourself” [[Custom instructions with AGENTS.md – Codex |
OpenAI Developers](https://developers.openai.com/codex/guides/agents-md)]. |
The most shocking fact is a terrifying research result that a poorly written instruction document has a much more severe negative impact than not having one at all. Researchers at Augment Code, who specialize in researching AI code-writing tools, conducted very systematic blind experiments. The result was that when provided with the most meticulously crafted, top-tier instruction document, the AI coding agent’s quality made a massive leap. This was a miraculous effect equivalent to suddenly upgrading an entire computer from a cheap, lightweight entry-level AI model to an AI model dozens of times more expensive and the smartest available.
| Conversely, the ‘worst files’ with messy rules spat out miserable garbage code that was even worse than having no guideline file at all. Sloppy and contradictory rules severely twisted the AI’s logical reasoning, causing it to actively erode its own abilities. The researchers strongly warned about the importance of writing proper documents, stating, “Most of what people copy from the internet and carelessly put into AGENTS.md is of no help to the AI and is rather actively hurting the AI’s capabilities” [[A good AGENTS.md is a model upgrade. A bad one is worse than no docs at all. | Augment Code](https://www.augmentcode.com/blog/how-to-write-good-agents-dot-md-files)]. |
Simply put, it’s a situation like this. A highly trained, smart genius dog (a massive AI model) is in front of you. If you give a short and clear instruction to this dog, “Bring the red frisbee in the yard to me” (a good AGENTS.md), the dog runs without hesitation and perfectly carries out the mission.
But what happens if you put too many useless words in the instruction document and lay out a bunch of confusing, conditionally intertwined rules like, “Bring the frisbee, but on your way, circle the apple tree on the right once, absolutely do not step in puddles, and only pick up the red frisbee, but if it’s blue bark three times, and if it’s around sunset don’t bring it…” (a bad AGENTS.md)? Even the smartest genius dog will fall into confusion, sit in place and whine, or bring back a random rock instead of the frisbee. Simply writing a lot of words in a document is never a good thing. Only concise rules that are well-organized, clear, and unambiguous can explode the AI’s potential and prevent fatal confusion.
What’s Next
As time passes, the brain intelligence of AI agents themselves will increase exponentially. However, it’s impossible for AI to independently and intuitively figure out the specific context required to smoothly and stably perform the ‘actual work’ that companies and individuals need in practice without any information. How to cleanly package the unique nuances and procedural know-how of a specific company, a specific team, and you, a specific user, and smoothly inject them into the AI will become the core competitive edge of future work.
Accordingly, moving forward beyond merely writing a single text document, advanced standardization technologies like ‘Skills’—which tightly bundle complex knowledge and context into software packages so that the AI can freely recall them whenever needed—are newly emerging and leading the market [A standardized way to give AIagentsnew capabilities and expertise.].
Also, this massive trend is rapidly moving beyond being exclusive to programmers who write code and is fiercely expanding into creative areas like design and planning. For example, to maintain visual design consistency rather than rigid code, ‘DESIGN.md’ files that define UI (User Interface) design tokens like a website’s font size, margins, and CSS color values are being widely shared as open source [VoltAgent/awesome-design-md: A collection of DESIGN.mdfiles…]. By actively utilizing this, a simple coding agent can be instantly transformed into a powerful ‘integrated design engine’ that even considers visual aesthetics [Open Design — Official open-source Claude Design alternative].
These changes present us with very important implications. In modern development collaboration tool environments like Builder.io, not only engineers who purely code, but also designers pondering the product’s exterior, or product managers (PMs) leading the entire project schedule will actively write and modify AGENTS.md containing their own requirements to collaborate with AI in real time [Improve your AI code output with AGENTS.md (+ my best tips)]. As the barriers of technology collapse, anyone will be able to teach their own work rules to AI and put it to work.
Of course, transitional technical issues are still occasionally discovered. Popular programming editors like Microsoft’s Visual Studio Code provide an auxiliary agent feature that quietly runs in the background analyzing code without catching the user’s eye [Using agents in Visual Studio Code]. However, in some developer forums, there are reports of error cases where these agents sometimes fail to properly read the rules in the AGENTS.md file, the core of the project, and end up spinning their wheels [Backgroundagentsdonot loadAGENTS.md… - Community Forum]. Nevertheless, these early bugs will naturally be resolved soon as agent ecosystem technology matures.
AI’s Take
In conclusion, in the workplace of the approaching future, the most important criterion separating the success and failure of individuals and companies will be “Do you have a custom guidebook that smartly teaches your complex work environment to your smart AI?” rather than “How much do you pay a month to subscribe to the smartest latest AI?”
We are in an era where considering what context to give your AI is far more important than blindly looking for the smartest AI. In the end, we must not forget that artificial intelligence producing the best performance is born not from a great algorithm, but from clear human instructions and systematic rules. In your work folder right now, is a kind guide for AI ready?
References
- GitHub - agentsmd/agents.md: AGENTS.md — a simple, open format for guiding coding agents
- AGENTS.md
-
[Custom instructions with AGENTS.md – Codex OpenAI Developers](https://developers.openai.com/codex/guides/agents-md) - A Complete Guide To AGENTS.md
- Improve your AI code output with AGENTS.md (+ my best tips)
- How to write a great agents.md: Lessons from over 2,500 repositories - The GitHub Blog
-
[A good AGENTS.md is a model upgrade. A bad one is worse than no docs at all. Augment Code](https://www.augmentcode.com/blog/how-to-write-good-agents-dot-md-files) -
[DoAGENTS.md/CLAUDE.mdFilesHelpCodingAgents? Towards AI](https://towardsai.net/p/machine-learning/do-agents-md-claude-md-files-help-coding-agents-a-new-paper-challenges-this) - VoltAgent/awesome-design-md: A collection of DESIGN.mdfiles…
- A standardized way to give AIagentsnew capabilities and expertise.
- Backgroundagentsdonot loadAGENTS.md… - Community Forum
- Open Design — Official open-source Claude Design alternative
- AGENTS.md & SKILL.md: The Complete Guide (2026)
- Instruction Files for AI Coding Assistants: An Overview
- Context Engineering 2026: AGENTS.md, CLAUDE.md, and .cursorrules That …
- Using agents in Visual Studio Code
- 30%
- 60%
- 90%
- Write it as vaguely and positively as possible, like 'You are a helpful assistant.'
- Specifically write explicitly executable commands.
- Set clear boundaries for what the AI must absolutely never do.
- Had no impact on performance.
- Produced worse results than having no instructions at all.
- Slightly improved performance.