AI Agents in Daily Work

Over the past year, I decided to try the magic of AI agents in my daily work (both in real projects and in several pet projects).

In this blog, I want to share what came out of it, the pros and cons of using AI in development, and whether it really helps or is just a “temporary whim.”

First of all, I will give a little background on my decision. Previously, I often used various IDE features and snippets (in particular from JetBrains) that simplified writing certain parts of the code, checked syntax, corrected grammar, and so on. It was not full-fledged AI, of course, but it helped a lot to avoid trivial errors, use more advanced syntax, and save time while writing code. However, when IDEs began to actively implement AI agents, I decided to try them as well.

Now let’s talk directly about AI.

Today, in almost every web or desktop development environment, we can see magic buttons and chats that promise to simplify our work and turn a routine set of code into a few minutes of waiting for a result – or even a finished program. But is AI really able to completely replace the developer, turning them into an outside observer who only sets parameters and receives a high-quality application as output? From my point of view, this question is rhetorical, and currently there is no clear answer to it.

Let’s look at it in more detail. On the one hand, we can give an AI agent a set of requirements, and the more detailed they are, the better result we will get. This certainly simplifies development in some ways, but on the other hand, the developer may lose an understanding of how the core of the program is built and how it works. AI generates a large amount of code, and sometimes you need to spend even more time understanding it than you would if you had written it yourself. In my opinion, this is the main problem when working with AI.

From my experience, AI can generate a fully working program quite well, but it takes a lot of time to understand the logic of the core, not to mention the fact that during execution, errors occur in almost 99% of cases, and the time spent on debugging and fixing them may be greater than the time required to write the code manually.

I have also often encountered situations where an AI agent produces code with obvious errors and then attempts dozens of times to find and fix them.

Some people may say that agents can be configured to reduce the number of possible errors and improve the quality of the generated code. That is true, but we still cannot trust such code 100%.

So, is it necessary to use AI to generate a production-ready application? It is quite difficult to give a definitive answer. However, I can say with confidence that the use of agents definitely has its advantages. Vibecoding cannot fully replace development yet, but it significantly simplifies simple tasks.

In my work, I often use AI to write tests, add comments, describe what has been done, and generate certain simple methods and small parts of the code. But I design the core architecture myself so that I clearly understand how the program should work.

To summarize: AI agents are still at an early stage of development and make mistakes very often, but they handle basic tasks well and free up the developer’s time to focus on the architecture of the system.

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