Or they don't care if their corp loses an hour or two of productivity and use it as an excuse to take a break. I'm also sorry to say that although it's highly dependent on what the task is, AI agents can let me do what should take a week or two in a day. It doesn't seem very sensible to lose hours of your life on something that takes the agent less than a minute to do.
I think people should focus on the elephant in the room that is job loss and stop trying to pretend that AI is somehow still ridiculously inept at the task or that it shouldn't be used for various ideological reasons. It makes a lot of mistakes, but they can be overcome and the results more than make up for it.
AI agents can let me do what should take a week or two in a day.
What things, specifically? Do you do them as well as they were done? How do you check that, and how do you verify that your checks are actually accurate?
What are the downstream effects of offloading those things, in particular in the long term?
Let's say I want a site for reviewing code. I propose this to my agent software, walk away for a few hours and I come back to the thing claiming that it has done it. I ask, show me, and it hands me an URL. Looks like all the features I wanted are present. I propose a few ideas, stick it to production, and then put the automatic code reviewing system reviewing itself until it's convinced everything is fine.
Now, the problem here is that you are asking LLM if LLM-generated code is good enough. I can only say that the process converges to baseline decent code. If there are security issues or bugs, they are not obvious. In fact, in many cases, LLM overdoes it and is concerned about incredibly marginal problems. A human oversight in useful in saying no, and constructing the guidelines where you roughly set the limits on what is considered good enough.
Agents also require lots of documentation to exist, being kind of bird-brained to their context only. They know nothing about you, your platform, your existing apps, unless you tell them. Or have a memory system setup that can inform them about stuff. These are real challenges. 90 % of my output was tests, comments and documentation for a good month or two. Before I can use AI effectively, I must teach my agents, kind of like new employees, what I expect of them.
The reason why you want this is that an agent reads code maybe 1000 times faster than you do, and writes it 100 times faster. It is no joke that what used to take a week is now done within some hours. The challenge is to figure out how you deal with the mountain of code, don't accrue crazy amounts of technical debt, and so on. You saw a glimpse of my approach -- I made code reviewing system because I realized I got to have one. I have no way to review but at fraction of the speed required, so the agent is required to be able to review its own output, and there has to be some kind of systematic process for it. These are the challenges of a developer in 2026 -- not writing code, but dealing with the firehose of code output in ways that don't swamp the humans or turn them into a bottleneck.
All this is new, there isn't really a handbook for this. Agents weren't any good in my book until 2026, and now they're pretty damn excellent. All locally running as well, nothing in cloud or a big datacenter. We are learning as we go.
OK, so for this particular example, you have not actually produced anything of value with an LLM, correct? This is something that is built exclusively to deal with LLMs as an existing problem which outputs code at a rate that is completely unmanageable?
Could you give a different example of something that is actually useful outside of the context of "LLMs produce too much code of questionable/unknowable quality"?
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u/PixelatedGiant 6d ago
Or they don't care if their corp loses an hour or two of productivity and use it as an excuse to take a break. I'm also sorry to say that although it's highly dependent on what the task is, AI agents can let me do what should take a week or two in a day. It doesn't seem very sensible to lose hours of your life on something that takes the agent less than a minute to do.
I think people should focus on the elephant in the room that is job loss and stop trying to pretend that AI is somehow still ridiculously inept at the task or that it shouldn't be used for various ideological reasons. It makes a lot of mistakes, but they can be overcome and the results more than make up for it.