The Current State of AI Coding Agents: From Auto-Completion to Repo-Wide Refactoring and Testing

On Friday afternoons at 4:30, a three-person backend team has a PR that hasn't been reviewed yet. The lead is busy fixing an urgent bug in the production environment, while the other two team members are stuck on each other's reviews. Two years ago, we would have said that the team is "short-staffed." However, in 2026, I would ask: among these twelve PRs, how many can actually be handled by a coding agent first, or even directly approved?

My deepest feeling over the past six months is that AI programming has gone beyond the stage of "automatic completion." It has evolved from a small helper that provides gray suggestions as you type to a "colleague" that can read the entire repository, modify multiple files, and even run tests automatically. This article will take you through the current state, differences, and usage of these tools in the first half of 2026.

Why this matters now

Let's start with a turning point: in the past, AI coding tools could only see the file in front of you, and maybe a few fragments you manually pasted in. They didn't understand your project structure, didn't know what your utility function was called, and didn't know if modifying file A would break file B. So, they were good at "writing a piece of code," but not at "modifying a project."

The biggest change in the first half of 2026 is that this contextual barrier has been broken down. Now, mainstream tools can index the entire repository, understand how files call each other, and modify them accordingly. You can say, "Replace the old payment flow with the new SDK," and it will find the relevant code scattered across six files and modify it together. This is what the industry calls "cross-file refactoring," and it's the key dividing line between "coding agents" and old-fashioned automatic completion.

For Taiwanese engineering teams, the importance of this is very practical. Many teams have small staff and multiple responsibilities, and reviews and refactoring are often the first to be squeezed out. Agents can take over these time-consuming, repetitive tasks that require a comprehensive understanding of the project. They won't replace the judgment of experienced engineers, but they will free people from tedious tasks.

Main tools and differences

I've categorized the tools that are often compared over the past six months based on "where they stand in your workflow":

• Cursor: Currently the most widely used AI programming editor, which looks like VS Code but is designed around AI. Its agent mode can read the entire project, modify files across them, and run commands. If you want a "main editor," it's usually the first recommendation.
• Windsurf: Also an AI-native editor, which focuses on the agent's ability to complete multi-step tasks smoothly. It's a direct competitor to Cursor, and the differences are mainly in operating feel and the interaction rhythm you're used to. It's recommended to try both before deciding.
• Factory: Takes a more "delegate the entire software development process to the agent" approach, covering not only coding but also engineering tasks from requirements to PR. Suitable for teams that want to integrate agents into their collaboration, rather than just using them as personal editors.
• Kilo Code: An open-source coding agent that often appears as a VS Code extension, allowing you to access agent capabilities in a familiar environment. Friendly to those who want to control models and costs themselves.
• cubic: Positioned towards AI code review, which automatically helps catch problems and provides suggestions when you open a PR. It's complementary to the "writing" tools above — one is responsible for production, and the other is responsible for quality control.

Remember, this field is changing rapidly, and each tool's functionality is being updated quickly. A more practical view is to first think about where you want the tool to stand (main editor? Team workflow? Review checkpoint?), and then choose.

How to use it (my own workflow)

To avoid being too abstract, I'll break down my actual workflow over the past six months:

1. Let the agent read the project first, rather than rushing to write code: When taking over an unfamiliar repository, I ask it to "show me the entry point of this project and how the main modules are divided," using it to quickly build a map.
2. Assign tasks with goals, rather than line-by-line instructions: I say, "Help me replace the user authentication from session to JWT, and make sure it's compatible with the old login API," rather than teaching it line by line. The agent's greatest value is that it will break down the tasks itself.
3. Submit in small steps and verify at any time: I don't let it modify twenty files at once. After modifying a segment, I ask it to run tests, and I review the diff to confirm the direction is correct before proceeding.
4. Throw the output to review: This step is often omitted, but it's crucial. Agents write quickly, but that doesn't mean they write correctly. I use review tools like cubic or the team's existing review process to review again. For how to choose review tools, we've written another article AI code review tools guide that you can refer to.
5. Diversify models: Different tasks are suitable for different models. High-difficulty architectural reasoning uses flagship models, while trivial batch modifications use cheaper and faster models. To achieve this diversification, you'll need a layer of basic infrastructure, which we discuss in detail in LLM infrastructure tools for 2026.

Common pitfalls and suggestions

Some pitfalls I've encountered, and also seen colleagues encounter:

• It will confidently modify the wrong things: Agents can sometimes be "overzealous," and you only ask it to fix one bug, but it modifies three unrelated files. Always review the diff, and don't blindly accept.
• Large projects can be confusing: The larger and more complex the repository, the higher the chance the agent will modify A and break B. The larger the task, the more you need to break it down into smaller segments and verify.
• Context is not always the more, the better: Putting the entire project into the agent doesn't necessarily make it smarter; it might even make it harder for it to grasp the key points. Learn to give it only the relevant files, and the effect is often better.
• Costs will accumulate quietly: The more aggressively you use these tools, and the more expensive the models, the faster the bills will rise. For team use, set up a budget and usage monitoring first.
• Don't let it touch critical code you don't understand: Security, payment, and permission-related areas — before the agent's code goes live, someone must truly understand it.

TheAI Academy perspective

My biggest takeaway over the past six months is that coding agents change not "who can write code" but "where engineers spend their time." After repetitive tasks are taken over, people should focus more on architectural decisions, requirement clarification, and quality control — things that agents still can't do well and won't replace in the short term.

Comment: 2026's coding agents are already capable but need to be supervised; treat them as junior colleagues to be guided, rather than as gods to be worshiped, and you'll truly save time.

Specific suggestions for Taiwanese readers: don't install five tools at once to compare. First, choose a main editor (Cursor or Windsurf) and use it for a month to develop the habit of "assigning goals, verifying in small steps, and reviewing." Once you're familiar with the agent's personality, you can consider whether to use Factory for team-level workflow or Kilo Code for cost control. Tools will keep changing, but the skills of "assigning tasks" and "verifying" won't expire. If you want to find more ready-made prompt templates, our prompt template library can be used directly.

References

• Cursor official documentation: https://docs.cursor.com
• Windsurf official website: https://windsurf.com

This article is a summary of tool categories and workflow explanations. Each tool's functionality is updated quickly, and actual capabilities and pricing should be based on the latest official announcements.