A coding agent can finish a task faster than you. Whether you can explain what it did or fix the code on your own is a different question.
Key Takeaways
- Coding agents can speed up a task without improving the user's understanding of the code they produce.
- In a small preprint, the weakest comprehension was linked to habits such as accepting edits without reading them, suggesting that review practices matter.
- Participants' confidence in their understanding didn't reliably match their performance, so self-assessment alone may miss comprehension risk.
What the Study Tested
A July 2026 preprint tested code comprehension among 54 students building small websites for a class. Participants used either a coding agent that edited the code directly or a chatbot whose suggestions they had to write or adapt themselves.
After the initial task, participants answered questions about the code they had produced and then extended that code without AI. The follow-up tested whether they could still work with the result once the AI tool was removed.
The paper has not been peer reviewed, so the findings should be treated as preliminary.
What They Found
- The agent group had an easier time completing the initial task, but didn’t understand the code as well. They scored lower on comprehension questions and struggled more when they had to extend the code without AI.
- The way participants used the agent mattered. The weakest comprehension showed up when they copied prompts without adapting them or accepted edits without reading them.
- Perception didn't always match performance. Participants still preferred the agent even when they performed worse on comprehension, so confidence wasn't a reliable signal of how well they understood the code.
Why Code Comprehension Becomes an Oversight Risk
Engineering teams also need to know whether people can review, explain, maintain, and extend agent-written code after it ships.
The study doesn't prove that professional engineering teams will have the same problem. But it does point to a behavior teams can address: accepting agent output without reading or adapting it. That makes review and verification practices an important part of an agent rollout rather than a reason to avoid coding agents altogether.
What to Watch For
Agent Effectiveness tracks review and verification behaviors that can help teams spot risky agent-use patterns, including whether verification happens before a change ships, how often engineers intervene in or correct a session, and where agents get stuck or drift.
Those behaviors can point to weak review practices, but they don't directly measure whether an engineer understood the code.
Until more evidence exists at enterprise scale, the practical takeaway is simple: watch how agent output is reviewed and verified, not just how much code the agent produces.
Frequently Asked Questions
Do coding agents reduce code comprehension?
We don't know yet. This small preprint found lower comprehension among students using a coding agent than among students using a chatbot, but it doesn't establish what happens with professional engineers or production code.
How can engineering teams reduce code-comprehension risk?
The study points to review behavior as one place to focus. Teams can set expectations that engineers read, verify, and understand agent-generated changes rather than accepting them automatically.
Monitor How Coding Agents Are Actually Used
Larridin's Agent Effectiveness helps engineering teams see how agents are used, including verification, interventions, corrections, and agent drift across workflows.