AI Agents for Developers: Which Tool Works Best in Real Projects? 

Today, it is hard not to notice how much the topic of AI has gained importance and how strongly it has influenced our daily work.

The world has become captivated by easy and almost immediate access to knowledge. At the same time, it constantly strives to improve successive tools to further increase the accuracy of responses and the comfort of work.

As a result, we have entered a stage of intense competition – every provider of AI solutions is trying to convince us to choose their product.

We want to benefit from the support of artificial intelligence, but more and more often, we no longer know what to choose.

Especially since AI in programming is no longer just about quick code suggestions. Increasingly, these are tools that act like a “co-worker” – analyzing problems, proposing solutions, and helping to go through the entire process of application development.

It is precisely these tools – AI agents – that we will take a closer look at.

In this article, I will show you what agents are available on the market, and you will see their comparison in practice based on the real experiences of our experts at House of Angular.

We hope this information will show you the different possibilities.

Popular AI agents on the market

AI agents are tools that support developers, capable not only of generating code but also of analyzing project context, proposing solutions, and performing specific tasks. Examples of such tools include Claude Code, Cursor, and GitHub Copilot.
They are most commonly used for:

• generating boilerplate code
• writing unit tests
• refactoring code
• debugging and error analysis
• rapid prototyping of functionality

In practice, they function like a “developer assistant” that speeds up work but still requires supervision and verification.

Cursor

Its greatest strength is working across the entire project – it can understand the context of multiple files and introduce changes on a larger scale. It is the closest to an environment where AI is an integral part of development.

Claude

More of a “conversational partner” than an editor. It handles analysis, explanation, and breaking problems down into parts very well. It is often used for thinking through a solution before writing any code.

GitHub Copilot

An assistant that works while writing code. Best for quick suggestions, completing functions, and everyday line-by-line work. The least “autonomous,” but the most direct.

Codex

The closest to a “task-executing agent.” Larger chunks of work can be delegated to it – from implementation to refactoring. It acts more like an executor than a suggester.

Our developers’ choices

We asked our developers for a short retrospective: which AI agent they started with, why they chose it, and what they use today.

Their experiences clearly show what the process of choosing a tool looks like in practice and what really matters.

What was your first choice when selecting an AI agent, and what encouraged you to choose it?

• The first choice was GitHub Copilot – the criteria were versatility, the ability to choose different language models (in practice, I mostly chose Claude Sonnet 4.6), integration with the IDE via a convenient plugin, and the price – $100/year for Copilot vs $200/year for Claude, or $10 vs $20 per month with a monthly subscription (in both cases, the annual subscription gives 2 months “free”).
  
• GitHub Copilot – code suggestions directly in the IDE – made a big impression on me at the time.
  
• GitHub Copilot – it was simply the first widely popular tool supporting programming with AI. At that point, Copilot didn’t yet have agentic capabilities, but even just the autocomplete and the idea of pair programming with AI were helpful and made a difference in day-to-day work.
  
• It all started with GitHub Copilot – inline suggestions convinced me, but over time, as its IDE plugin evolved, I increasingly started using the built-in AI agent available there.
  
• GitHub Copilot – good integration with JetBrains IDEs, code generation and autocomplete in the same package.
  
• GitHub Copilot, due to integration with WebStorm (I couldn’t switch to VS Code). At that time, intelligent code completion itself made a huge impression and genuinely sped up daily work… until the first contact with Cursor.
  
• At the beginning, I tested Copilot and ChatGPT. Copilot seemed fine for code suggestions, but after some time, it started lagging badly (maybe an IDE issue), and I eventually stopped using it – it was faster to write something myself than to wait for a suggestion. I chose ChatGPT mainly because of its popularity and the fact that everyone was talking about it.
  • Interestingly, during a temporary loss of confidence in WebStorm and a brief switch to VS Code, Copilot worked COMPLETELY differently. Much better, much faster. Honestly, if it worked that way in WebStorm as well, I might have changed my opinion about it.

• My first choice was ChatGPT. It could explain how a piece of code/function works or write a simple method. That was enough for me during work.
  
• The initial tool was GitHub Copilot. The first fascination with AI. Although it often made mistakes, it still helped a lot with repetitive tasks. For those times, it was impressive. With the emergence of new tools, I completely stopped using it.

Which tools do you currently use most often and in what situations?

• Currently, I use Claude Code, mainly for monotonous, repetitive tasks such as syntax updates, writing tests, and generating mocks for tests.
  
• Claude Code – daily tasks, refactoring, planning, and implementation of larger functionalities, writing tests.
  
• These days, I mostly use Claude Code – mainly for implementing repetitive tasks, supporting refactoring, and writing tests. I also really value its planning mode, especially for more complex tasks and for analysing, reviewing, and summarising code. I still use Copilot as well, but mainly as autocomplete or for more local, ad hoc changes or quick questions.
  
• Claude Code – I basically use it daily whenever there is a reasonable opportunity. I try to involve it in all repetitive tasks or implementations of functionalities similar to existing ones. For new challenges, I very often ask for its opinion, and if the model understands the problem/idea well enough, I also delegate the implementation to it.
  
• Cursor – implementing/editing smaller functionalities, writing unit tests or stories for Storybook, improving/adding accessibility in components. For more complex tasks, I describe the architecture and implementation for each file to the agent and create a template of the final solution, which I then refine to meet project quality standards. In hobby projects, I use Codex.
  
• Cursor and Claude Code IDE – I use Cursor when I already have a specific flow in mind, as it reads intentions very well (TAB development). I use Claude Code for more difficult tasks. In my opinion, it handles the larger context better than Cursor. For complex tasks, I use the Opus model, even though the same model is available in Cursor. The code generated by Claude is of higher quality.
  
• Mainly Claude (Sonnet due to the cost of Opus) + Codex.
  I started with Codex because I already had a ChatGPT subscription – and it was really solid at the beginning, especially for code generation. You give it specific instructions, and it gets the job done. It also handles unit tests very well.
  • Out of curiosity, I later checked Claude and compared both tools. Ultimately, today, if I use AI, in ~95% of cases, it is Claude.
  • I mainly use it for: generating boilerplate, writing unit tests, sometimes for more complex problems like “<<something>> doesn’t work, <<something>> should work this way. Fix it.”

• Currently, I use Codex. It comes bundled with ChatGPT Plus. It helped me with large-scale migrations and creating stories for Storybook.
  
• For a long time now, my favorite has been Claude Code. A tool that can confidently be called a precursor, setting trends and methodologies for working with AI. By default, I use Sonnet for fast prototyping, refactoring, optimization of individual code fragments, and tests. For more demanding tasks, the planning mode also works great, and its effectiveness can be further boosted by switching the model to Opus. I have a rich library of side projects where I test various tools. When working on them, I use Stitch to create a design. From time to time, I run Antigravity to check the current level of Google’s models. New LLMs are constantly emerging, so for benchmarking and testing, I use OpenRouter, which provides easy access to most top models. Recently, I have also been exploring the capabilities of Open Code, an open alternative to Claude Code, with the possibility of adapting models other than those from Anthropic.

How do you evaluate this tool in your daily work?

[Claude]
For the use cases described above, in my case it significantly increases work efficiency. However, I have not yet used this tool for more “ambitious” and complex tasks.

[Claude]
I treat Claude Code as a tool supporting daily work. It understands project context well and handles more complex tasks effectively. Thanks to the use of skills (https://angular.dev/ai/agent-skills), it supports working with the latest Angular very well, but I still sometimes have to make manual corrections to the generated code.

On a daily basis, I use the Sonnet model – Opus would exhaust the token limit in the Pro plan too quickly, while Sonnet allows me to work for most of the day without reaching that limit.

[Claude]
I consider Claude Code a solid support in daily work, helping boost efficiency and avoid mistakes. The results largely depend on how well you get to know the tool (kudos to Anthropic docs) and how well you can use it – proper configuration (there’s a ton of it), supporting tools, providing context, or simply the ability to prompt well. Output naturally varies depending on the type of task, so it’s worth drawing conclusions and adjusting your approach accordingly.

In terms of context limits and token efficiency – it would be great to always go full power, but in reality, I just needed to find my own balance. Choosing the right model helps – I use mainly Sonnet and Opus, but sometimes Haiku – along with more conscious context management.

On the downside: getting new features is great, but in my opinion, the pace is almost too fast. I’m also concerned about Anthropic’s silent changes to tool and model parameters, which are easy to miss but do affect capabilities, stability, and the overall experience.

[Claude]
Indispensable for rapid prototyping, generating ideas, and handling repetitive tasks. When it comes to new functionalities, I believe you still need to be able to assess when a task is suitable for full agent-based generation and when it’s better to only partially rely on it. With the right choice of instructions and tasks, you can save a lot of time.

That said, you still can’t expect it to do everything for you — continuous supervision and corrections are still required. Additionally, one downside I notice daily is the quality of IDE integration – not only is it minimal, but it also doesn’t always work as expected.

[Cursor]
For ambitious tasks, I mainly use the Sonnet 4.6 model, and for smaller things, I use the Composer model. I am satisfied with the quality of the generated code. The tool is worth its price – it offers integration with agents, excellent UX, and autocomplete that, in my opinion, is far ahead of the competition. The only downside is token limits, which can be restrictive – but that’s the case with any AI tool with monthly usage limits.

[Cursor and Claude Code IDE]
In daily work, both of these tools can speed up development and understand context very well – but the cost of token usage is high, especially for larger tasks, so it’s worth splitting work into stages.

[Claude and Codex]
Claude generally works best when you tell it exactly what, where, and how to do something. Without that, it can drift a bit.

The code usually still needs to be reviewed, because sometimes something that could be done in 2 lines ends up taking dozens.

Despite that, it definitely speeds up work.

The biggest advantages:

• handles console/terminal errors very well (e.g., during migrations)
• very good for refactoring
• unit tests are a total game-changer

Writing meaningful tests used to take so much time that they were sometimes skipped. Now, with AI, it feels almost wrong not to write them, because it takes just a moment.

[Codex]
Compared to ChatGPT, it is a game-changer, mainly because it sees the project context and can adapt code style to the existing one. However, during repetitive work, after some time, it has its “drifts” and loses track, so you have to re-explain what it should do, where, and how.

Codex works with VS Code, and I am a WebStorm fan, so I use Codex in VS Code but actually code in WebStorm.

[Claude]
Claude Code has a real impact on improving a developer’s work. Not only for performing monotonous tasks, but also as support in creating advanced code. However, the tool requires some familiarity and practice, and the codebase needs to be properly prepared for working with AI to fully unlock Claude Code’s potential. The number of available features and commands is fully sufficient for me, and I consider it satisfactory. That doesn’t mean it is without flaws. In CLI-based solutions, you can feel certain limitations, such as the lack of an easy way to reference multiple specific lines or code fragments at once, which would not be an issue in IDE-based tools.

What is this tool better at compared to previous ones?

[Claude]
A wide range of configuration options (agents, skills, etc.), better reasoning and context memory, and more granular usage limits (per session/week vs per month in Copilot).

[Claude]
Above all, a significantly better understanding of context.

[Claude]
Honestly, after getting used to UI/IDE based AI tools, I had some reservations about switching to the terminal. But despite some typical CLI limitations, it quickly became my preferred interface for the tool. The biggest value is the completeness of the ecosystem, the configurability, and the quality of results you can achieve. In my experience, even when using Anthropic models with other harnesses, the results weren’t nearly as good – which shows that both the Claude models and the harness are doing a great job here.

[Claude]
Excellent context understanding, very advanced tooling, and configurability.

[Cursor]
Cursor’s Composer model quickly edits and generates boilerplate files. Better UX compared to CLI tools with IDE add-ons. In addition to agent features, Cursor has incredible autocomplete that almost magically predicts what I will do next, which is especially useful when refining code after AI during review. In my opinion, it is currently the best tool combining agent-based code generation with direct IDE integration.

[Cursor and Claude Code IDE]
Compared to the Copilot version I used at the beginning, it offers better context understanding and generates higher-quality code.

[Claude and Codex]
Claude handles more complex problems much better than Codex – it can “think more broadly,” but this also means it consumes more tokens.

Codex, on the other hand, is more “straightforward” – it overthinks less and delivers simple things faster, such as:

• code generation
• tests
• simple refactoring

Example: I had a simple case – migrating two states into one (some duplication of props). At some point, Claude started overcomplicating things with some providers and used ~20% of tokens for the next 5 hours.

Codex handled the task in a few minutes – cheaper and faster.

On the other hand, if you have bugs or something more complex, Codex doesn’t perform as well.

[Codex]
It has insight into the project context and generates solutions tailored to a specific case. It is suitable for simple and repetitive refactoring / migrations / writing tests.

[Claude]
I believe the Claude “ecosystem” itself does a great job. The tool (CLI) can easily be replaced with, for example, the previously mentioned Open Code. Native support for the best models on the market is key here. Anthropic continuously expands its portfolio with new tools, which directly translates into work quality and satisfaction. Examples include Remote Control for remote terminal handling or the recently released Claude Design. By investing in any package, you get not only access to the most powerful models but also valuable extras.

Would you switch this tool for another one?

[Claude]
A wide range of configuration options (agents, skills, etc.), better reasoning and context memory, more granular usage limits (per session/week vs per month in Copilot).

[Claude]
At the moment, I don’t see an alternative.

[Claude]
I don’t feel a need to do so now, but there’s so much evolving that – never say never.

[Claude]
As far as I know, there is currently no better tool on the market, and minimal differences are not worth the time investment. Until there are clear opinions about a strong advantage of another tool, I don’t see a need to switch.

[Cursor]
From the Agentic IDE space, I tried Antigravity, but I think Cursor is better. Terminal tools like Codex and Claude Code are great, but in my opinion, they lack a better UX for quick changes in single files. I probably wouldn’t switch this tool, but I might additionally get Claude Code to reduce token usage in Cursor when building larger functionalities from scratch.

[Cursor and Claude Code IDE]
The ideal setup for me would be the interface and ergonomics of WebStorm, the autocomplete and speed of Cursor, and the context understanding and code quality of Claude Code.

[Claude and Codex]
At the moment, no.

[Codex]
I see many opinions that Claude is significantly better, and its subscription cost is very similar. I think it’s worth trying.

[Claude]
I don’t feel particularly attached to Claude. If something better appears that meets my expectations, I would probably switch. At the moment, nothing like that is on the horizon.

Would you like to share any additional experiences or opinions? Maybe you have some advice for people looking for the right agent for themselves?

• I wouldn’t go for an annual subscription to a single tool. At the current pace of AI development, it may turn out that much better options will be available in a year – and that money will be wasted.
  
• It’s worth keeping an eye on what’s happening in the AI market, but I wouldn’t get too caught up in it – there’s so much marketing and FOMO around this space that it’s easy to lose perspective. I think it’s better to find what makes you more productive and comfortable, and stick with that choice for a while. It gives you time to really get to know the tool and optimize the way you use it – and much of that experience carries over regardless of what you end up using next.
  
• In my opinion, it’s not worth constantly jumping between tools. In their raw form, they usually offer similar capabilities, and what really matters is how well we can use their potential. It’s better to spend time learning and configuring properly than constantly comparing, because only then can you assess whether another tool can actually offer more.

• Configure your repository for the agent so it has feedback on what it generates — skills, MCP, Husky, ESLint, NX generators, and tests give agents insight into what they are doing wrong and what they can improve in the generated code, so that it is high-quality and meets business requirements. When choosing a tool, look not only at code generation but at the entire ecosystem. Features such as pull request reviews or bug detection can catch issues before you submit code for team review, saving time for the whole team.
  
• A lot depends on how someone wants to use AI and their preferences – the best approach is simply to test a few options and see what works best.
  • It’s also worth looking at new prototyping tools (competition for Figma). For example, Stitch from Google or the new Claude Design may shake things up significantly. I tested Stitch and it already made a very good impression. Claude will probably go even further.
    
• It’s worth testing different tools yourself and checking which one you work with best. Choosing a tool is only the first step – it is followed by configuration and building an agentic workflow tailored to your needs. It’s worth keeping up with new developments and staying up to date, as the industry is constantly evolving – new models, patterns, and standards. Not long ago, we were all reading opinions on LinkedIn about how prompt engineers would dominate the IT world and take our jobs. Who writes prompts today?

Key takeaways

Based on our developers’ responses, we can observe several recurring patterns:

• For most, the first contact with AI was GitHub Copilot – good availability, relatively low cost, strong integration with popular IDEs, and support for pair programming
• Currently, the most commonly used agent is Claude Code – strong understanding of project context, ability to work on larger parts of the application, code analysis, and very good performance on complex problems
• Most common use cases: generating boilerplate, writing unit tests, refactoring, migrations, error analysis
• The tool itself is not the most important factor, but rather how it is used – providing proper context, the ability to write good prompts, breaking problems into smaller parts, and proper project configuration

At the same time, these tools are not without drawbacks. Developers point out:

• the need to verify the generated code
• models tend to lose context or look for overly complex solutions
• token limits that restrict work
• CLI tools can be less convenient than those integrated with IDEs

Most developers do not declare a willingness to switch from their current tools, especially in the case of Claude Code, which is perceived as one of the most complete solutions on the market.

In the context of recommendations, several recurring pieces of advice appear:

• avoid constantly switching between tools; instead, learn one well and optimize your own workflow
• due to the rapid pace of AI development, it is better not to commit to an annual subscription
• preparing the repository for working with AI is important, as it can increase agent efficiency (ESLint, tests, CI, generators, feedback loop for the agent)
• test different ways of working (CLI / IDE / hybrid)
• remember the purpose of agents – they are tools to speed up work, not to fully automate it

AI Agent Comparison

Comparison of agent pricing (April 2026)

Cheapest: Copilot

•  ~$10/month
•  no additional fees
•  very predictable cost
  
  → Best “entry-level” option

Mid-tier: Claude / Cursor / Codex

• ~$20/month starting price
  but:
  •  Claude → tokens limit
  • Cursor → may charge overage
  • Codex → often usage-based
    
    → Actual cost depends on usage

Most expensive scenarios (power user)

•  Claude: up to $100–200/month
•  Cursor: can generate high usage costs
•  teams: even $1000+/month
  
  → With intensive usage, AI becomes expensive

Key differences in the pricing model

Copilot (simplest)

•  you pay a fixed amount
• you don’t think about tokens
•  less control, but no surprises

Claude / Codex (more “AI-native”)

• you pay for capabilities + usage
• more power, but:
  •  you need to manage context
  •  you can “burn through” your budget

Cursor (hybrid)

• subscription + additional costs
• very convenient UX
  but:
  • cost can be unpredictable

Summary

The analysis clearly shows that AI is no longer a novelty, but has become a real part of a developer’s daily work. The shift from simple suggestions like GitHub Copilot to full-fledged agents such as Claude Code or Cursor has changed the way we approach writing code.

It is no longer just about speeding up individual lines, but about:

• delegating tasks
• working with a larger context
• support in decision-making

At the same time, there is no single “best” tool. Each of them has its place:

• some work well for fast code writing,
• others for analysis and refactoring,
• and others for “flow” work in the IDE.

However, the most important conclusion remains unchanged – it is not the tool that makes the difference, but the way it is used.

Developers who achieve the best results:

• consciously choose tools for the task
• can describe the problem well
• control and verify results
• optimize their workflow

The prices of these tools are relatively low at the entry level, but they differ in pricing models:

• GitHub Copilot → ~ $10/month, predictable cost
• Claude Code → ~ $20/month, but with limits and possible higher-tier plans
• Cursor → ~ $20/month, often with additional usage costs
• Codex → dependent on usage (API / subscription)

In practice:

• the cheapest tool is not always the most cost-effective
• costs increase with usage intensity
• many developers optimize expenses by combining several tools

Finally, it is worth remembering that the AI market is developing very dynamically. What is a standard today may become just one of many solutions in a few months.

Therefore, instead of looking for the “perfect tool,” it is better to:

• focus on understanding how to work effectively with AI
• build your own workflow
• treat AI as support, not a replacement

Let us remember that ultimately, it is still the developer who is responsible for quality, architecture, and decisions.

Special thanks to my colleagues from House of Angular for sharing their experiences and thoughts on working with AI.

Thanks to you, readers can look at the topic from a practical perspective, see different approaches, and draw concrete and useful conclusions.