How vibe coding really works
Vibe coding is a new way to build software. Instead of writing code by hand, you describe the goal in English. AI does the rest for you.
Many people think it is just simple prompting. But the real power of vibe coding is somewhere else. It is a system of work, not a one-time task.
The company AI w Biznesie teaches its clients how to build such a system. Thanks to this, AI works like a permanent team member, not just a tool.
Levels of working with AI
Beginners versus advanced users
Beginners write a prompt and wait for the result. Then they check the code and write another prompt. This works, but it is very slow.
Advanced users automate the whole workflow. AI does the repetitive steps without asking. This saves hours every day.
The difference is huge. One prompt is one task. Automation is hundreds of tasks without your help.
Plan first, code later
The best AI programmers start with a plan. They do not ask for code right away. First they create a document with requirements and steps.
The plan should live in the project repository. It is a reference point for all decisions. AI comes back to it for every task.
With a plan you avoid chaos. AI knows what to do and why. This is the base of good vibe coding.
Tools you should know
Cursor and Codex
Cursor is one of the best tools for coding with AI. It supports models from different companies, like OpenAI and Anthropic. It also has its own model.
Codex stands out with beautiful design and short descriptions. AI shows in one sentence what it just did. You do not need to read long essays.
Both tools have a cloud agent feature. These are agents that work in the cloud, not on your computer. This way you can run many tasks at once.
Claude Code and other options
Claude Code is also a good tool. But it has a usage limit that runs out quickly. That is why some people use it less often.
Devin and Factory are other great options. Each has its own strengths and weaknesses. It is worth trying a few and picking what works for you.
The company AI w Biznesie helps its clients choose the right tools. There is no single best solution for everyone.
Rules and configuration files
agents.md and Claude.md
Files like agents.md are a way to set rules for AI. You tell the tool how to work and how to communicate. It is like a manual for the agent.
In the file you can define response style, commit structure, and coding preferences. AI remembers these rules and uses them for every task.
Most tools support agents.md. The exception is Claude Code, which has its own file called Claude.md. It works the same way but has a different name.
How to set rules in Cursor
In Cursor you go to preferences and click the rules button. There you can write how communication should look. For example: answer short and simple.
You can also add project rules. Avoid writing scripts in static files. Do not use fake data outside of tests. This keeps work organized.
Cursor learns your preferences over time. It saves them in the agents.md file. The more you use the tool, the better it understands you.
Skills – your superpowers
What are skills
Skills are ready-to-use instructions for AI. If you do something more than once, turn it into a skill. This saves time and removes mistakes.
Instead of copying the same prompt, you type a slash and the skill name. AI does the whole task automatically. It is simple and effective.
There are thousands of ready-made skills on the market. You can download them and use them right away. One of the most popular has 61 thousand stars on GitHub.
What to use skills for
Skills are great for repetitive tasks. For example, to review code before sending. Or to run tests.
You can also create skills for company rules. If your company has a specific writing style, save it in a skill. AI will use it every time.
Tools can also be skills. If you have a specific way to use an API, define it once. AI will know how to call it.
Automations and loops
How automations work
Automations are skills that run when events happen. For example, after opening a pull request. AI gets an instruction and does the task.
In Cursor and Codex, automations are built in. You pick a trigger, write an instruction, and it is ready. AI waits for the right moment and acts.
Example: after opening a PR, an automation waits for comments from Greptile. Then it addresses the comments and sends fixes. All without your input.
Loops – work until done
Loops are processes that repeat until a condition is met. They have a trigger, an action, and a final goal. AI works until it reaches the goal.
Example: a nightly loop checks the documentation. It compares code with documents and updates missing parts. It stops when everything matches.
Another example: a loop optimizes pages. It loads each page and measures time. If it is too slow, it optimizes and checks again. It runs until everything is fast.
The company AI w Biznesie created a library of ready-made loops. You can use it for free. It is a collection of proven solutions.
Cloud agents versus local agents
Advantages of cloud agents
Cloud agents run on servers, not on your computer. You can start 20 agents at once without slowing down your machine. That is a huge benefit.
They are available from anywhere. Most tools have mobile apps. You can manage agents from your phone.
Cloud agents work in isolated environments. They do not conflict with each other, even when working on the same code. This removes many problems.
When local agents are better
Local agents are faster. The environment is already ready on your computer. You do not have to wait for a server to start.
You have more control over files. You see what changes happen in real time. This gives a sense of safety.
New features often appear first in local agents. The cloud gets them later. If you like new things, local might be better.
Work trees – a way to avoid conflicts
What are work trees
A work tree is a separate copy of your repository. Each agent gets its own copy to work on. This way they do not overwrite each other’s changes.
Without work trees, agents edit the same files. This leads to conflicts and errors. Work trees solve this problem.
Creating a work tree is simple. In Cursor you click a button and pick a new copy. The agent works in its own environment.
When you do not need work trees
If agents work on different parts of the code, work trees are not needed. For example, one does the front end, another the back end. They do not conflict.
In small projects with one agent, you also do not need work trees. This is a solution for advanced users with many agents.
The company AI w Biznesie recommends work trees for team work. It is a standard in professional vibe coding.
Multi-model workflow
Why use different models
Not every model is good at everything. One is great at planning, another writes code, and a third does reviews. Use them where they are best.
This saves money. The best models are expensive. Use them only for hard tasks. For simple things, a cheaper model is enough.
It also speeds up work. Light models work faster. You do not have to wait for a heavy model on a simple task.
Example workflow with multiple models
You plan a new feature with the Fable model. It sees the whole code and creates a detailed plan. This requires a lot of power.
For writing code you use the Composer model. It follows instructions very well. It does not need to see the whole project, just its part.
Code review is done with the GPT 5.5 model. It gives a fresh look at what Composer wrote. It is like a second programmer checking the work.
The problem with merge and deploy
Why it is hard
When many agents finish work at the same time, problems start. Each one wants to push its code to the main branch. This causes blocks.
One agent merges, CI and deploy start. Another waits. Then it has to rebase and test again. This takes time.
The more agents, the worse it gets. Every new merge forces the others to restart. This is an unsolved problem in the world of vibe coding.
How to deal with it
One way is patience. You let agents work one after another. It is slow, but it works.
Another way is batching changes. One agent collects all changes and merges them together. This reduces the number of conflicts.
Cursor is working on its own Git system for agents. This might solve the problem in the future. For now we have to manage on our own.
Best practices for everyday work
Tests, documentation, and logs
There is no reason to have weak code. Set up an automation that checks tests. If coverage is missing, AI writes new tests.
Documentation can also be automatic. Every night an agent checks if the code matches the documents. It updates missing parts without your help.
Log everything. It costs very little and gives huge value. An agent can analyze errors and create fixes. You wake up and the problem is already solved.
Three pillars of good code
Excellent tests, excellent documentation, and excellent logs. These are three pillars that protect your project. AI can maintain all of them automatically.
The company AI w Biznesie teaches its clients this principle. When these three elements work, the rest is easy. You can focus on new features.
This is not theory. It is practice used by the best companies in the world. Nvidia, Zapier, Brex – all use similar systems.
Summary
The best way to vibe code is not better prompting. It is building a work system with AI. Plan, rules, skills, automations, loops, tests, and logs.
When you set that up, AI is no longer chaos. It becomes a production process for building software. You save time, money, and stress.
Start with small steps. Set rules in agents.md. Create one skill. Run one automation. You will see how fast it works.
The company AI w Biznesie can help you implement these solutions. Check our services and see how vibe coding can change your work.
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