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AI coding assistants or editors, such as Cursor, Windsurf, Lovable, and GitHub Copilot, are transforming how developers write code. You can now turn an idea into a working app in minutes just by typing a few prompts. That's exciting but also risky. Many new developers can now build features without really understanding how the code works. Can you trust what the AI writes? Will you or your team understand it later? In some cases, the AI is making big decisions about how the software architecture is built, not the developer.
Usually, senior engineers do not jump straight into coding without considering domain knowledge, architecture, or code reusability. They know when a piece of code fits and when it doesn't. To be useful for real projects, AI tools need to provide developers with more structure, control, and ways to test and trust what gets built.
In this article, I will explore existing problems with AI-assisted coding (or some people call it vibe coding) and what the AI editor experience should look like for senior software engineers.
The Problem With Most AI Coding Tools Today
AI coding tools have shown us that language models can write code. Most of them today aim to save time and automate routine tasks. AI can automate up to 80% of the work, but achieving 99% or higher accuracy still depends on human input. Because in the end, the most valuable part of your codebase isn't the code. It's the thinking behind it. Let's review some key AI coding problems.
1. The AI Misunderstands Your Intent
The AI never fully understood what you wanted to build. You type a prompt like "create an endpoint that returns active users." The AI confidently writes some code. But what does "active" mean in this context? Last login? Session time? Subscription status?
The AI gives you a half-right solution without understanding the full intent. If you try to provide highly detailed prompts, it is too costly with a token-based pricing model and effort-intensive for the user. Or the AI gets into loops, forgets the prompt halfway. Now you spend more time debugging code you didn't write.
2. The AI Doesn't Explain Its Choices
Where did this API call come from? What's the structure of this function? Why did it choose this library? These questions go unanswered because most AI tools provide output without rationale. As a result, senior developers are left auditing unfamiliar code with no insight into the assumptions or trade-offs behind it, which makes modifying the output risky. When nobody owns or understands the reasoning behind the code, the code loses its long-term maintainability.
3. No Task Structure, No Planning
Coding is not just typing. It's decomposing a problem, making architectural decisions, and thinking through edge cases. Most vibe coding tools I tried generate code in a single block, without breaking the work down into logical steps or providing visibility into what's been completed and what's left.
There's no task progress dashboard or overview of completed versus pending actions. You blindly click "Next" without knowing how much is done or left. It encourages a passive relationship with the AI, where the developer becomes a reviewer instead of a collaborator.
4. Testing Comes Too Late (Or Not At All)
AI tools rarely test what they write. If they do, it's often surface-level. That means more bugs, more manual effort, and more risk. For senior developers shipping production code, these problems make AI feel more like a junior intern than a reliable teammate.
What Senior Developers Really Need From AI
AI tools shouldn't just type fast. They should support the way experienced developers build and maintain software, with structure, feedback loops, and domain awareness.
1. Plan: Align Before You Code
Senior developers usually don't jump straight into code  --  they clarify scope, break work into pieces, and align on what's being built. AI tools should do the same by asking the right questions, clarifying the scope, and creating a task plan with subtasks. This Plan phase helps solve one of the biggest pain points in AI coding: misalignment.
2. Code and Verify: Don't Just Generate, But Also Test, Fix, Repeat
It's not enough for code to compile. Every time the AI generates code, it should also verify that it works through unit tests and functional testing for different workflows.
This process should be automatic and repeatable, like a Code-Verify Loop (as it's depicted in the picture below*)*:
3. Don't Just Write Code  --  Own It
Senior developers build software that evolves with the business. That requires aligning code with business intent, domain terms, and organizational standards. To help senior engineers, AI-generated content should come with context:
* What was generated, and how does it connect to the goal?
* Why was this method or library chosen?
* What changed compared to the existing implementation?
* What trade-offs were made  --  performance vs clarity, speed vs flexibility, etc.?
This level of clarity is essential, especially if you want to maintain the code for a longer period. Inline comments, code diffs, and simple changelogs should be part of the output.
4. Secure, Sandboxed Environments
For enterprise and professional developers, trust in tooling doesn't just come from output quality, but it comes from how safely that output is generated.
Yet many AI tools today default to cloud-based processing, often uploading a large part of the code to external servers. For teams handling sensitive data, proprietary code, or regulated environments, this is a dealbreaker.
* Code should run and test in a secure sandbox.
* Avoid uploading source code to external servers.
* Testing should work in isolated environments, enabling experimentation without risk.
The Future of AI Coding Is Collaborative
What senior engineers need is not just a typing assistant. They need a reliable, explainable, and autonomous teammate  --   an agent that plans before coding, tests every step, explains decisions made, and adapts to the context of the project. This is how real development works. We're not far from this future. But getting there means rethinking the entire AI coding experience and starting to build AI agents that earn their place on the team.
I'd love to hear your thoughts:
