The AI era is already here, but many companies still evaluate engineers with traditional LeetCode-style interviews.
People spend huge amounts of time grinding Hot100 problems and repeatedly practicing handwritten algorithm questions. In real engineering work, especially as AI Coding and Coding Agents become more capable, this kind of training is increasingly disconnected from actual software delivery.
In the past, maybe we had fewer options. Writing code itself was expensive, so engineering ability was often reduced to a simple question: can you write the code yourself? As a result, solving algorithm problems became a required cost for many people entering the industry.
But the AI era should not keep working this way.
The scarce skills in the AI era are not only about handwriting code. They are about whether you can:
- Turn a requirement into clear, executable engineering work.
- Drive AI / Agents to complete a task until it is shippable.
- Understand whether AI-generated code is actually correct.
- Find hidden bugs, edge cases, compatibility issues, and security risks.
- Decide whether a pull request is truly safe to merge.
- Add tests that cover real risk instead of only increasing test count.
- Take responsibility for AI-generated code like a senior engineer.
That is the starting point for AgentCode.
AgentCode aims to become a new kind of practice ground: not another place where people mechanically grind algorithm problems, but a place where people practice the skills that matter in the Agent era. The goal is to help people become AgentCoders for the Agent era.
As AI makes writing code cheaper, judging whether code can safely ship becomes more valuable.
Traditional coding platforms mainly ask:
Can you write the correct code yourself?
AgentCode asks:
Can you use AI to deliver a correct, tested, mergeable change?
And:
Can you tell whether AI-generated code is actually safe to ship?