Today you can spin up several AI agents in minutes and hand them tasks in parallel. We use this every day: the agents find the right spots in a project, prepare repetitive changes, write tests, and take over part of the routine that used to eat up hours. The speed-up is real. But it comes with a risk that is easy to miss precisely because the result looks professional.
Where AI lets you down
Obvious nonsense is not the scary part: you spot it right away. Far more dangerous is tidy code that compiles, passes a surface check, and solves not quite the task you asked for. An agent may call a similar but wrong API method, forget a rare scenario, or write a test that confirms its own implementation rather than the original requirement. On the outside it all looks convincing.
We do not see how the model arrived at its solution, and we cannot assume it held the whole project context in mind: the architectural constraints, the old agreements, the quirks of the data, and the consequences for neighbouring parts of the system. So a confident answer from the agent is not proof to us but a draft that still has to be checked.
A plausible mistake is more dangerous than an obvious one. The obvious kind gets noticed and fixed. The plausible kind slips through: the code looks so convincing that nobody double-checks it - and it sails calmly into the product, only to surface later at the client's end.
What happens after the agent's answer
The change goes through the same path as code written by a human. An engineer reads the diff, checks it against the task, examines the debatable spots, and runs the necessary tests. If the solution touches architecture or data, a single green check in the tests is not enough: you need to understand why exactly this approach was chosen and what will happen at the edges of the scenario.
No change reaches the product on the argument that "the agent did it that way." The author of the solution is still the engineer: it is he who must be able to explain the code and answer for its consequences.
Why this turns out better, not slower
Review takes back part of the time saved on writing the code, but it does not cancel out the whole gain. The agent makes the first pass quickly and takes on the volume; the engineer spends attention where it is truly needed: on the requirements, the edges, and the consequences of the solution. Without that check, the gain easily turns into deferred debugging after the release.
That is why, for us, AI is not a replacement for the engineer but a tool that raises his pace. The faster it produces changes, the more important it is not to loosen quality control.
An AI agent is speed without responsibility. The engineer adds the responsibility by reading every line - and only together do they produce code you can trust.
