I do this too, but then you need some method to handle it, because now you have to read and test and verify multiple work streams. It can become overwhelming. In the past week I had the following problems from parallel agents:
Gemini running an benchmark- everything ran smoothly for an hour. But on verification it had hallucinated the model used for judging, invalidating the whole run.
Another task used Opus and I manually specified the model to use. It still used the wrong model.
This type of hallucination has happened to me at least 4-5 times in the past fortnight using opus 4.6 and gemini-3.1-pro. GLM-5 does not seem to hallucinate so much.
So if you are not actively monitoring your agent and making the corrections, you need something else that is.
You need a harness, yes, and you need quality gates the agent can't mess with, and that just kicks the work back with a stern message to fix the problems. Otherwise you're wasting your time reviewing incomplete work.
Here is an example where the prompt was only a few hundred tokens and the output reasoning chain was correct, but the actual function call was wrong https://x.com/xundecidability/status/2005647216741105962?s=2...
Glancing at what it's doing is part of your multitasking rounds.
Also instead of just prompting, having it write a quick summary of exactly what it will do where the AI writes a plan including class names branch names file locations specific tests etc. is helpful before I hit go, since the code outline is smaller and quicker to correct.
That takes more wall clock time per agent, but gets better results, so fewer redo steps.
Here is an example where the prompt was only a few hundred tokens and the output reasoning chain was correct, but the actual function call was wrong https://x.com/xundecidability/status/2005647216741105962?s=2...
I as a human have typos too - and sometimes they're the hardest thing to catch in code review because you know what you meant.
Hopefully there is some of lint process to catch my human hallucinations and typos.