alt Hacker NewsAI handles incidents, engineers lose touch with their systems
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This article is all hypothesis and no evidence.
Yes, it's clear that if AI agents handle routine issues then only the most complex issues will be handled by humans. In no way does it follow that humans will be less prepared to handle those incidents than they are now; by definition, this class of problems have no easy known solutions, and require deep system knowledge to remediate.
What we are seeing here is an instance of the more general trend where experts are still required, but AI destroys the career path which creates experts. That's what we should be worrying about.
I see aviation sentiment raised from time to time. In aviation failure is catastrophic, and systems you operate do not change on the fly (pun intended).
You can probably drill SREs that way but you are only teaching them how to _react_ efficiently, not how to _fix_ unique unicorn root causes of these failures. Aviation analogy would be an airplane engineer that tries to drill himself for in-flight engine failures AND engine debugging / engine redesign both at the same time.
It never made sense in software engineering, and never will. Minute spent on drills is a minute better spent on reworking codebase to reduce changes of future incidents. This post is very SRE centric.
This has already happened in other areas. I'm old enough to remember when customer service people spent all day handling customer problems themselves, which gave them the understanding needed to solve more unusual problems.
These days they do what the computer tells them. Even if they could solve the problem they don't have the agency. They can only select from the options they are given.
In software we still have the agency but we are giving away the understanding. The agency will follow.
I find the use of AI like quicksand.
The more I use it, the more I have to rely on it to make changes/ fix things in the same system. In the end, I come out feeling empty; no intuitive knowledge of the system "I" built or fixed.
Code review is important but it does not replace the mental model I am able to build when I do all the steps of software development manually without AI.
I feel a lot of comments here are missing the forest for the trees.
We do not yet have the next generation systems that will manage AI creation and maintenance of systems.
Humans have been making spaghetti code systems and maintaining them poorly for years. Best practices developed… eventually. But certainly not in the 70s and 80s. Spaghetti was the norm for quite some time.
The development paradigm has changed. Forever. You can’t expect yesterday’s tools for managing software development to succeed at this point. We’re still roughly on year one of this transformation.
The new bottlenecks are creating and enforcing boundaries in the code, identifying level of risk within a boundary, subjecting high risk areas to more intense human review and architecture reworks, and so so much more manual testing.
We need a new language for high level development that focuses on architectural constraints. We need analysis software that helps draw boundaries, identify what needs extended human attention, and helps us map and understand a rapidly developing code base. We need to standardize on the use of frameworks and languages like we have on assembly variants.
I’ll bring up a hacker news trope here. NaughtyDog’s GOAL was an amazing system that was a product of an underdeveloped ecosystem for game development at the time. They used lisp and assembly because you still had to write assembly at the time.
We are in a very similar time. The ecosystem is underdeveloped. We should be looking for new languages and tools to manage this.
CNC machines used to run from punch cards and then hand written NC code. Now we have advanced CAM software. (Which innovative people are actually running with LLMs!)
The more code writes autonomously, the less intuition the human owners have about that code. Loss of intuition is a seed of technical debt that grows with time. Over a long enough horizon, it can make looking at your own codebase feel like the first day on the job (sometimes at a company you started).
Luckily, there are ways to mitigate this and essentially translate those human intuition of how the codebase “should” be into guardrails for the agents. But without that, your setting your sails in a stochastic sea where each wave looks nothing like the last.
I’m not sure who will be solving these incidents if 99% of incidents will be “solved” by LLMs. If I’m called once a year my daily rate will be my yearly rate?
This is why the paradigm for AI use should not be automation but rather the cyborg. Under automation, people are less active and engaged and become mere operators of automated processes. They become slaves of the machines. Under the cyborg model, they arrange the machines in a way to make people masters of a universe that includes the machines helping them be that.
Code too.
I work with programmers and it's not uncommon that they can remember with shocking detail about code they've written in the past.
Someone might mention an issue that has cropped up and they'll stare off into space for a few moments and actually remember where that issue stems from in the code, because they remember writing it like 8 months ago.
This skill will be lost when AI is generating all code, we'll be stuck in a perpetual loop of having AI keep track of the state of the code in order for AI to extend and maintain it.
I've seen a variation of this where random engineers are pulled into production incident calls and engineers are not expected to be familiar with the system.
They were asked to "just use AI" and understand the component, triage the issue, build a fix etc. The engineer was forced to choose between accepting a potentially mediocre fix AI has suggested or risk being coming across as an incompetent resource who doesn't know how to leverage AI.
You can guess what the engineer chose. The fix wasn't bad but it was suboptimal for some edge cases. We had to later revise it. Have enough of these situations, engineers will eventually definitely give up understanding the system in detail.
If capability increase continues as it has, then an incident that cannot be resolved by AI will stump humans no matter the practice.
I like the plane example from the article,but I think in reality it will be like code. 1.5 years ago engineers would routinely say that they still write code by hand here or there to keep their skills sharp, and that's just not something you hear much if at all.
If an SRE is faced with a situation an AI can't solve, then said SRE will use the AI systems to triage further, point it to different places and so on.
This works for SREs with pre-AI experience and intuition, possibly less so with new recruits coming in post-AI. I don't know what the solution to this is, maybe practice drills is it, but I have a hunch the entire field will be subsumed, same as many other engineering fields.
There is only so much need for taste and judgement, before even that has been incorporated into the models.
Anyone who's worked in tech in a large company will probably have experienced having an ops team who use RPA tools to do repetitive tasks that tech teams get the blame for when things break. AI will make this so much worse. Things will break, everyone will assume 'tech knows the system', but really it's a new process outside of the tech teams that someone vibe coded but got it wrong.
Audit trails, logs, and tight data governance where things can only be accessed with proper roles is the only possible solution.
If an RPA team ever gets direct access to a production database in your company, look for a new job.
When someone else -whether AI agent or a human- solves the recurring minor problems for you, those problems become non-issue, get swept under the rug, just to accumulate more dust.
One day, those may become bigger as they are forgotten, causing havoc. The standard root-cause-analysis depending on systems having certain retention period, which may be expired at that time.
It is important to get real hold of one's systems from end-to-end aspect, which holds true for both AI and human operators...
Meta: The blinking cursor of the "logo" of the blog being sticky in the top left corner makes it impossible for me to read the text. It constantly fires interrupts at me.
Depending on what your goals as the author are, you may or may not want that.
Being able to scroll it out of view might be enough to achieve the aesthetics goal, and the goal of people actually listening to you.
We have been running Agents on infrastructure and letting to create resources, scale up and down, security scans etc.
I agree with premise of thr blog. The question i have been asking internal does knowing your system really matter if you can recreate it in minutes.
We recently had a situation, where in with our internal platform and claude we recreated everything in minutes.
Management in the end cares about the outcome and not how the meat is made.
Isn't there anywhere to "go" from here? In the last decades, introducing new high level abstractions on top of existing paradigms naturally had everyone move up the ladder and work at the next higher level, why should this be different these days? Do we think AI will reach the top of the abstraction ceiling, so there's no where to go from here?
You’d hope these AI incident responders have very constrained production tools to fix things. You’d hope the humans remain familiar with those tools and they are incredibly well documented.
The article does not get the point. What aircraft companies did was separate training and work, and SRE/IT typically does not.
An AI can handle routine incidents and then present learning cases from that routine work for training, because the skill in SRE is not the mechanical log grepping, grafana dashboard browsing etc but forming the hypothesis. AI incident reports can create training cases that are a much better training for hypothesis forming and testing than the work itself can.
"Middle devs take care of incidents, senior devs lose touch with their systems"
Prediction: this won’t happen. The abstraction will be good enough and people will need to know only as much as they need to know- things will stabilise at the equilibrium.
You rather generously assume engineers are in touch with their systems.
Even before layoffs many teams just maintained things org has long lost coherent knowledge of
After layoffs and typical org knowledge churn - you can either rewrite it (but how? Product team responsible for original implement requirements is long gone too) or recoup (reverse document) some of that lost knowledge with AI and actually learn
Ironies of Automation is front and center in this article which is awesome. Many of the conversations on AI automation are describing or rediscovering the insights the paper covered.
Good article and I like the callouts to the aviation industry. For me what's missing is the author should also have touched on CRM and SRM.
Also, that paper "The Ironies of Automation" is one that everyone should read. It's fairly short.
There is a related problem in terms of these situations where the computer system is handing off to the human. It's called "the bumpy transfer of control." Very fascinating concept.
That‘s the goal of the AI tech bro world: get us dependant on their tech, ruin our native/raw skills, lock us into their proprietary skills.
Reminds me of the move from on prem to cloud. Linux sysadmins were killed and replaced by aws focused devops.
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I am exceedingly tired of poor metaphors that are popping up since AI has taken over writing.
No, operating software is not like operating a plane. Not at all in fact. The people operating the software and resolving incidents are the same people who created the software in the first place, and continue to work on it day to day. Pilots have not and don't.
A natural evolution of engineers losing touch with the customers and users.
I'm noticing some of the concern play out regarding AI weakening the capabilities of software people.
I gave the team an exact solution on a silver platter and they still failed to identify how to go about it after 3 days slamming it into Claude. The resolution is literally 1 line of code that could be arrived at in about 30 minutes of patient, old school troubleshooting.
I think what's happening is the AI system draws poorly aligned and led engineers into this ego inflation feedback loop where they are completely detached from reality because these tools can simulate a better one.