Link does not work for me but as someone who does a lot of work with LLMs I am also betting against agents.
Agents have captivated the minds of groups of people in each large engineering org. I have no idea what their goal is other then they work on “GenAI”. For over a year now they have been working on agents with the promise that the next framework that MSFT or Alphabet publishes will solve their woes. They don’t actually know what they are solving for except everything involves agents.
I have yet to see agents solve anything but for some reason this idea that having an agent that you can send anything and everything will solve all problems for the company. LLMs have a ton of interesting applications but agents have yet to grasp me as interesting, I also don’t understand why so many large companies have focused time around it. They are not going to be cracking the code ahead of a commercial tool or open source project. In the time spent toying around with agents there are a lot of interesting applications that could have built, some of which may be technically an agent but without so much focus and effort on trying to solve for all use cases.
Edit: after rereading my post wanted to clarify that I do think there is a place for tool call chains and the like but so many folks I have talked to first hand are trying to create something that works for everything and anything.
A4ET8a8uTh0_v2 · 35m ago
<< I also don’t understand why so many large companies have focused time around it. They are not going to be cracking the code ahead of a commercial tool or open source project.
I think it is a mix of fomo and the 'upside' potential of being able to minimize ( ideally remove ) the expensive "human component". Note, I am merely trying to portray a specific world model.
<< In the time spent toying around with agents there are a lot of interesting applications that could have built, some of which may be technically an agent but without so much focus and effort on trying to solve for all use cases.
Preaching to the choir man. We just got custom AI tool ( which manages to have all my industry specific restrictions rendering it kinda pointless, low context making it annoying, and slower than normal, because it now has to go through several layers of approval including 'bias' ).
At the same time, committee bickers over minute change to a process that has effectively no impact on anything of value.
Bonkers.
wooque · 16m ago
>I have no idea what their goal is
goal is to fire you (human), decrease costs and increase profits
infecto · 2m ago
That’s a bit reductive and misses the core issue. Of course companies want to reduce headcount or boost productivity, but many are pursuing these initiatives without a clear problem in mind. If the mandate were, say, “we’re building X to reduce customer support staff by 20%,” that would be a different story. Instead, it often feels like solution-first thinking without a clear target.
johnisgood · 57m ago
I have no idea what agents are for, could be my own ignorance.
That said, I have been using LLMs for a while now with great benefit. I did not notice anything missing, and I am not sure what agents bring to the table. Do you know?
mhog_hn · 52m ago
An agent is an LLM + a tool call loop - it is quite a step up in terms of value in my experience
infecto · 43m ago
Not a disagreement with you but wanted to further clarify.
I do think it’s a step up when done correctly. Thinking of tools like Cursor. Most of my concern and issue comes from the amount of folks I have seen trying to great a system that solves everything. I know in my org people were working on Agents without even a problem they were solving for. They are effectively trying to recreate ChatGPT which to me is a fools errand.
johnisgood · 48m ago
What is the use case? What does it solve exactly, or what practical value does it give you? I am not sure what a tool call loop is.
infecto · 42m ago
Cursor is my classic example. I don’t know exactly what tools are defined in their loop but you give the agent some code to write. It may search your code base, it may then search online for third party library docs. Then come back and write some code etc.
kro · 43m ago
The tools can be an editor/terminal/dev environment, automatically iterating to testing the changes and refining until a finished product, without a human developer, at least that is what some wish of it.
Link is working for me — perhaps it was not 30 minutes ago? (Safari, MacOS)
globular-toast · 27m ago
I think in general if everyone is talking about a solution and nobody is talking about problems then it's a sign we're in a bubble.
For me the only problem I have is I find typing slow and laborious. I've always said if I could find a way to type less I would take it. That's why I've been using tab completion and refactoring tools etc for years now. So I'm kind of excited about being able to get my thoughts into the computer more quickly.
But having it think for me? That's not a problem I have. Reading and assimilating information? Again, not a problem I have. Too much of this is about trying to apply a solution where there is no problem.
RamblingCTO · 29m ago
I also build agents/ai automation for a living. Coding agents or anything open-ended is just a stupid idea. It's best to have human validated checkpoints, small search spaces and very specific questions/prompts (does this email contain an invoice? YES/NO).
Just because we'd love to have fully intelligent, automatic agents, doesn't mean the tech is here. I don't work on anything that generates content (text, images, code). It's just slob and will bite you in the ass in the long run anyhow.
Simon_O_Rourke · 2h ago
Don't tell management about this, as they're all betting the house on AI agents next year.
pmg101 · 1h ago
Only one of these outcomes will be correct, so worth putting money on it if you think they're wrong a la The Big Short.
DavidPiper · 1h ago
Not OP, but I've been thinking about this and concluded it's not quite so clear-cut. If I was going to go down this path, I think I would bet on competitors, rather than against incumbents.
My thinking: In a financial system collapse (a la The Big Short), the assets under analysis are themselves the things of value. Whereas betting on AI to collapse a technology business is at least one step removed from actual valuation, even assuming:
1. AI Agents do deliver just enough, and stay around long enough, for big corporations to lay off large number of employees
2. After doing so, AI quickly becomes prohibitively expensive for the business
3. The combination of the above factors tank business productivity
In the event of a perfect black swan, the trouble is that it's not actually clear that this combination of factors would result in concrete valuation drops. The business just "doesn't ship as much" or "ships more slowly". This is bad, but it's only really bad if you have competitors that can genuinely capitalise on that stall.
An example immediately on-hand: for non-AI reasons, the latest rumors are that Apple's next round of Macbook Pros will be delayed. This sucks. But isn't particularly damaging to the company's stock price because there isn't really a competitor in the market that can capitalise on that delay in a meaningful way.
Similarly, I couldn't really tell you what the most recent non-AI software features shipped by Netflix or Facebook or X actually were. How would I know if they're struggling internally and have stopped shipping features because AI is too expensive and all their devs were laid off?
I guess if you're looking for a severe black swan to bet against AI Agents in general, you'd need to find a company that was so entrenched and so completely committed to and dependent on AI that they could not financially survive a shock like that AND they're in a space where competitors will immediately seize advantage.
Don't get me wrong though, even if there's no opportunity to actually bet against that situation, it will still suck for literally everyone if it eventuates.
conartist6 · 57m ago
If you want to bet on a competitor, let's talk cause I'm your guy. While everyone else was looking the other way, I stole home: https://github.com/bablr-lang
ptero · 39m ago
While true, the world doesn't end in 2025. While I would also agree that big financial benefits from agents to companies appear unlikely to arrive this year (and the title specifically mentions 2025) I would bet on agents becoming a disruptive technology in the next 5-10 years. My 2c.
corentin88 · 34m ago
Why this timeline? What’s missing today that would make it possible in 5-10 years?
Quarrelsome · 40m ago
shorting only works if people realise it when you do. c-suite will run out of make up before admitting its a pig because the pay off is huge for them. I reckon agentic dev can function "just enough" to allow them to delay the reality for a bit while they fire more of their engineering team.
I don't think this one is worth shorting because there's no specific event to trigger the mindshare to start moving and validating your position. You'd have to wait for very big public failures before the herd start to move.
The fundamental difference is we need HITL to reduce errors instead of HOTL which leads to the errors you mentioned
KoolKat23 · 2h ago
Human multi-step workflows tend to have checkpoints where the work is validated before proceeding further, as humans generally aren't 99%+ accurate either.
I'd imagine future agents will include training to design these checks into any output, validating against the checks before proceeding further. They may even include some minor risk assessment beforehand, such as "this aspect is crucial and needs to be 99% correct before proceeding further".
a_bonobo · 2h ago
That's what Claude Code does - it constantly stops and asks you whether you want to proceed, including showing you the suggested changes before they're implemented. Helps with avoiding token waste and 'bad' work.
KoolKat23 · 2h ago
thats good to hear, theyre on their way there!
on a personal note, I'm happy to hear that. I've been apprehensive and haven't tried it, purely due to my fear of the cost.
csomar · 2h ago
Lots of applications have to be redesigned around that. My guess is that micro-services architecture will see a renaissance since it plays well with LLMs.
paradite · 2h ago
This is obviously AI generated, if that matters.
And I have an AI workflow that generates much better posts than this.
Retr0id · 2h ago
I think it's just written by someone who reads a lot of LLM output - lots of lists with bolded prefixes. Maybe there was some AI-assistance (or a lot), but I didn't get the impression that it was AI-generated as a whole.
paradite · 2h ago
"Hard truth" and "reality check" in the same post is dead giveaway.
I read and generate hundreds of posts every month. I have to read books on writing to keep myself sane and not sound like an AI.
squigglydonut · 1h ago
Absolutely! And you're right to think that. Here's why...
kookamamie · 1h ago
Applogies! You're exactly right, here's how this spans out…
Retr0id · 2h ago
True, the graphs are also wonky - the curves don't match the supposed math.
jrexilius · 1h ago
The thing that sucks about it is maybe his english is bad (not his native language) so he relies on LLM output for his posts. Im inclined to cut people slack for this. But the rub is that it is indistinguishable from spam/slop generated for marketing/ads/whatever.
Or it's possible that he is one of those people that _realy_ adopted LLMs into _all_ their workflow, I guess, and he thinks the output is good enough as is, because it captured his general points?
LLMs have certainly damaged trust in general internet reading now, that's for sure.
paradite · 35m ago
I am not pro or against AI-generated posts. I was just making an observation and testing my AI classifier.
fleebee · 32m ago
The graphs don't line up. I'm inclined to believe they were hallucinated by an LLM and the author either didn't check them or didn't care.
Judging by the other comments this is clearly low-effort AI slop.
> LLMs have certainly damaged trust in general internet reading now, that's for sure.
I hate that this is what we have to deal with now.
delis-thumbs-7e · 1h ago
I wonder why a person from Bombay India might use AI to aid with an English language blog post…
Perhaps more interesting is whether their argument is valid and whether their math is correct.
mritchie712 · 32m ago
> I've built 12+ production AI agent systems across development, DevOps, and data operations
It's hard to make *one* good product (see startup failure rates). You couldn't make 12 (as seemingly a solo dev?) and you're surprised?
we've been working on Definite[0] for 2 years with a small team and it only started getting really good in the past 6 months.
> Each new interaction requires processing ALL previous context
I was under the impression that some kind of caching mechanism existed to mitigate this
blackbear_ · 1h ago
You have to compute attention between all pairs of tokens at each step, making the naive implementation O(N^3). This is optimized by caching the previous attention values, so that for each step you only need to compute attention between the new token and all previous ones. That's much better but still O(N^2) to generate a sequence of N tokens.
ilaksh · 50m ago
Yes, prompt caching helps a lot with the cost. It still adds up if you have some tool outputs with long text. I have found that breaking those out into subtasks makes the overall cost much more reasonable.
_heimdall · 2h ago
Caching would only help to keep the context around, but caching would only be needed if it still ultimately needs to read and process that cached context again.
It that just avoids having to send the full context for follow-up requests, right? My understanding is that caching helps to keep the context around but can't avoid the need to process that context over and over during inference.
No comments yet
stpedgwdgfhgdd · 1h ago
Compact the conversation (CC)
csomar · 2h ago
My understanding is that caching reduce computation but the whole input is still processed. I don’t think is fully disclosing how their cache works.
LLMs degrade with long input regardless of caching.
snappr021 · 40m ago
The alternative is building Functional Intelligence process flows from the ground up on a foundation of established truth?
If 50% of training data is not factually accurate, this needs to be weeded out.
Some industries require a first principles approach, and there are optimal process flows that lead to accurate and predictable results. These need research and implementation by man and machine.
digitcatphd · 1h ago
I’m sure most of the problems cited in this article will be easily solved within the next five years or so, waiting for perfection and doing nothing won’t pay dividends
Xmd5a · 1h ago
>A database query might return 10,000 rows, but the agent only needs to know "query succeeded, 10k results, here are the first 5." Designing these abstractions is an art.
It seems the author never used prompt/workflow optimization techniques.
> AI tools aren't perfect yet. They sometimes make mistakes, and they can't always understand what you are trying to do. But they're getting better all the time, In the future, they will be more powerful and helpful. They'll be able to understand your code even better, and they'll be able to generate even more creative ideas.
This phrase is usually followed by some, you know...Math?
Gigachad · 1h ago
The article is slop. That’s just a phrase ChatGPT uses a lot.
constantcrying · 1h ago
No, it is not "mathematically impossible". It is empirically implausible. There is no statement in mathematics that says that agents can not have a 99.999% reliability rate.
Also, if you look at any human process you will realize that none of them have a 100% reliability rate. Yet, even without that we can manufacture e.g. a plane, something which takes millions of steps, each without a 100% success rate.
I actually think the article makes some good points, but especially when you are making good points it is unnecessary to stretch credibility with exaggerating your arguments.
john_minsk · 37m ago
Valid point, however the promise of AI is that it will be able to manufacture a metaphorical “plane” for each and every prompt user inputs I.e. give 100% overall reliability by using all kinds of techniques (testing, decomposing etc) that intelligence can come up with.
So until these techniques are baked into the model by OpenAI, you have to come up with these ideas yourself.
macleginn · 51m ago
This is a good point, but it seems, empirically, that most parts of a standard passenger airplane have reliability approximating 100% in a predefined time window with proper inspection and maintenance, otherwise passenger transit would be impossible. When the system does start to degrade, e.g. because replacement parts and maintenance becomes unavailable or too costly (cf. the use of imported planes by Russian airlines after the sanctions hit), incidents quickly start piling up.
constantcrying · 42m ago
It's about what you do with errors. If you let them compound they lead to destruction, if instead you inspect, maintain, reinspect, replace, etc. you can manage them.
My point was that something extremely complex, like a plane, works, because the system tries hard to prevent compounding errors.
vntok · 2h ago
> Production systems need 99.9%+ reliability
This is not remotely true. Think of any business process around your company. 99.9% availability would mean only 1min26 per day allowed for instability/errors/downtime. Surely your human collaborators aren't hitting this SLA. A single coffee break immediately breaks this (per collaborator!).
Business Process Automation via AI doesn't need to be perfect. It simply needs to be sufficiently better than the status quo to pay for itself.
navane · 1h ago
It's not just about up time. If the bridge collapses people die. Some of us aren't selling ads.
vntok · 1h ago
If "the bridge collapses and people die" because the team has a 1min26 "downtime" on a specific day, which is what you are arguing, then you have much bigger problems to solve than the performance of AI agents.
Pasorrijer · 2h ago
I think you're crossing reliability and availability.
Reliability means 99.9% of the time when I hand something off to someone else it's what they want.
Availability means I'm at my desk and not at the coffee machine.
Humans very much are 99.9% accurate, and my deliverable even comes with a list of things I'm not confident about
vntok · 1h ago
> Humans very much are 99.9% accurate
This is an extraordinary claim, which would require extraordinary evidence to prove. Meanwhile, anyone who spends a few hours with colleagues in a predominantly typing/data entry/data manipulation service (accounting, invoicing, presales, etc.) KNOWS the rate of minor errors is humongous.
satyrun · 27m ago
Yea exactly.
99.99% is just absurd.
The biggest variable though with all this is that agents don't have to one shot everything like a human because no one is going to pay a human to do the work 5 times over to make sure the results are the same each time. At some point that will be trivial for agents to always be checking the work and looking for errors in the process 24/7.
lexicality · 2h ago
Currently I'm thinking about how furious the developers get any time Jenkins has any kind of hiccough, even if the solution is just "re-run the workflow" - and that's just network timeouts! I don't want to imagine the tickets if the CI system started spitting out hallucinations...
lerchmo · 1h ago
Alot of deterministic systems externalize their edge cases to the user. The software design doesn’t fully match the reality of how it gets used. Ai can be far more flexible in the face of dynamic and variable requirements.
hansmayer · 2h ago
This may not be about internal business processes. In e-commerce 90 sec can be a lot of revenue lost, and mission-critical applications such as telecommunications or air control, it would be downright a disaster (ever heard of five nines availability)?
dmezzetti · 2h ago
It's clear that what we currently call AI is best suited for augmentation not automation. There are a lot of productivity gains available if you're willing to accept that.
deadbabe · 1h ago
I just want someone to give me one legit use case where an AI Agent now enables them to do something that couldn’t be done before, and actually makes an impact on overall profit.
roschdal · 2h ago
AI is for people without natural intelligence.
satyrun · 7m ago
Yea just average IQ like Terence Tao.
All you are really saying with this comment is you have an incredibly narrow set of interests and absolutely no intellectual curiosity.
bboygravity · 2h ago
So it's for 90+ percent of society?
Sounds like good business to me.
block_dagger · 2h ago
Downvotes are for comments like yours
rvz · 2h ago
Let's get a timer to watch this fall off the front page of HN in minutes.
"We can't allow this post to create FUD about the current hype on AI agents and we need the scam to continue as long as possible".
saadatq · 1h ago
we need a flag button for “written by AI”.
I’m at this stage where I’m fine with AI generated content. Sure, the verbosity sucks - but there’s an interesting idea here, but make it clear that you’ve used AI, and show your prompts.
rvz · 21m ago
... and it's gone. Stopped the timer on 2 hours and 38 mins.
vntok · 2h ago
Generally speaking, low quality posts don't spend too much time on the front page, regardless of their topic.
cmsefton · 1h ago
2015? The title should be 2025.
RustyRussell · 1h ago
2015? Title is correct, this is a typo
tomhow · 1h ago
Sorry about that, my fault, moderating from my phone.
Agents have captivated the minds of groups of people in each large engineering org. I have no idea what their goal is other then they work on “GenAI”. For over a year now they have been working on agents with the promise that the next framework that MSFT or Alphabet publishes will solve their woes. They don’t actually know what they are solving for except everything involves agents.
I have yet to see agents solve anything but for some reason this idea that having an agent that you can send anything and everything will solve all problems for the company. LLMs have a ton of interesting applications but agents have yet to grasp me as interesting, I also don’t understand why so many large companies have focused time around it. They are not going to be cracking the code ahead of a commercial tool or open source project. In the time spent toying around with agents there are a lot of interesting applications that could have built, some of which may be technically an agent but without so much focus and effort on trying to solve for all use cases.
Edit: after rereading my post wanted to clarify that I do think there is a place for tool call chains and the like but so many folks I have talked to first hand are trying to create something that works for everything and anything.
I think it is a mix of fomo and the 'upside' potential of being able to minimize ( ideally remove ) the expensive "human component". Note, I am merely trying to portray a specific world model.
<< In the time spent toying around with agents there are a lot of interesting applications that could have built, some of which may be technically an agent but without so much focus and effort on trying to solve for all use cases.
Preaching to the choir man. We just got custom AI tool ( which manages to have all my industry specific restrictions rendering it kinda pointless, low context making it annoying, and slower than normal, because it now has to go through several layers of approval including 'bias' ).
At the same time, committee bickers over minute change to a process that has effectively no impact on anything of value.
Bonkers.
goal is to fire you (human), decrease costs and increase profits
That said, I have been using LLMs for a while now with great benefit. I did not notice anything missing, and I am not sure what agents bring to the table. Do you know?
I do think it’s a step up when done correctly. Thinking of tools like Cursor. Most of my concern and issue comes from the amount of folks I have seen trying to great a system that solves everything. I know in my org people were working on Agents without even a problem they were solving for. They are effectively trying to recreate ChatGPT which to me is a fools errand.
See https://ampcode.com/how-to-build-an-agent
For me the only problem I have is I find typing slow and laborious. I've always said if I could find a way to type less I would take it. That's why I've been using tab completion and refactoring tools etc for years now. So I'm kind of excited about being able to get my thoughts into the computer more quickly.
But having it think for me? That's not a problem I have. Reading and assimilating information? Again, not a problem I have. Too much of this is about trying to apply a solution where there is no problem.
Just because we'd love to have fully intelligent, automatic agents, doesn't mean the tech is here. I don't work on anything that generates content (text, images, code). It's just slob and will bite you in the ass in the long run anyhow.
My thinking: In a financial system collapse (a la The Big Short), the assets under analysis are themselves the things of value. Whereas betting on AI to collapse a technology business is at least one step removed from actual valuation, even assuming:
1. AI Agents do deliver just enough, and stay around long enough, for big corporations to lay off large number of employees
2. After doing so, AI quickly becomes prohibitively expensive for the business
3. The combination of the above factors tank business productivity
In the event of a perfect black swan, the trouble is that it's not actually clear that this combination of factors would result in concrete valuation drops. The business just "doesn't ship as much" or "ships more slowly". This is bad, but it's only really bad if you have competitors that can genuinely capitalise on that stall.
An example immediately on-hand: for non-AI reasons, the latest rumors are that Apple's next round of Macbook Pros will be delayed. This sucks. But isn't particularly damaging to the company's stock price because there isn't really a competitor in the market that can capitalise on that delay in a meaningful way.
Similarly, I couldn't really tell you what the most recent non-AI software features shipped by Netflix or Facebook or X actually were. How would I know if they're struggling internally and have stopped shipping features because AI is too expensive and all their devs were laid off?
I guess if you're looking for a severe black swan to bet against AI Agents in general, you'd need to find a company that was so entrenched and so completely committed to and dependent on AI that they could not financially survive a shock like that AND they're in a space where competitors will immediately seize advantage.
Don't get me wrong though, even if there's no opportunity to actually bet against that situation, it will still suck for literally everyone if it eventuates.
I don't think this one is worth shorting because there's no specific event to trigger the mindshare to start moving and validating your position. You'd have to wait for very big public failures before the herd start to move.
The fundamental difference is we need HITL to reduce errors instead of HOTL which leads to the errors you mentioned
I'd imagine future agents will include training to design these checks into any output, validating against the checks before proceeding further. They may even include some minor risk assessment beforehand, such as "this aspect is crucial and needs to be 99% correct before proceeding further".
on a personal note, I'm happy to hear that. I've been apprehensive and haven't tried it, purely due to my fear of the cost.
And I have an AI workflow that generates much better posts than this.
I read and generate hundreds of posts every month. I have to read books on writing to keep myself sane and not sound like an AI.
Or it's possible that he is one of those people that _realy_ adopted LLMs into _all_ their workflow, I guess, and he thinks the output is good enough as is, because it captured his general points?
LLMs have certainly damaged trust in general internet reading now, that's for sure.
Judging by the other comments this is clearly low-effort AI slop.
> LLMs have certainly damaged trust in general internet reading now, that's for sure.
I hate that this is what we have to deal with now.
Perhaps more interesting is whether their argument is valid and whether their math is correct.
It's hard to make *one* good product (see startup failure rates). You couldn't make 12 (as seemingly a solo dev?) and you're surprised?
we've been working on Definite[0] for 2 years with a small team and it only started getting really good in the past 6 months.
0 - data stack + AI agent: https://www.definite.app/
I was under the impression that some kind of caching mechanism existed to mitigate this
They don't go into implementation details but Gemini docs say you get a 75% discount if there's a context-cache hit: https://cloud.google.com/vertex-ai/generative-ai/docs/contex...
No comments yet
LLMs degrade with long input regardless of caching.
If 50% of training data is not factually accurate, this needs to be weeded out.
Some industries require a first principles approach, and there are optimal process flows that lead to accurate and predictable results. These need research and implementation by man and machine.
It seems the author never used prompt/workflow optimization techniques.
LLM-AutoDiff: Auto-Differentiate Any LLM Workflow https://arxiv.org/pdf/2501.16673
> AI tools aren't perfect yet. They sometimes make mistakes, and they can't always understand what you are trying to do. But they're getting better all the time, In the future, they will be more powerful and helpful. They'll be able to understand your code even better, and they'll be able to generate even more creative ideas.
From another post on the same site. [0]
Yup, slop.
[0]: https://utkarshkanwat.com/writing/review-of-coding-tools/
This phrase is usually followed by some, you know...Math?
Also, if you look at any human process you will realize that none of them have a 100% reliability rate. Yet, even without that we can manufacture e.g. a plane, something which takes millions of steps, each without a 100% success rate.
I actually think the article makes some good points, but especially when you are making good points it is unnecessary to stretch credibility with exaggerating your arguments.
So until these techniques are baked into the model by OpenAI, you have to come up with these ideas yourself.
My point was that something extremely complex, like a plane, works, because the system tries hard to prevent compounding errors.
This is not remotely true. Think of any business process around your company. 99.9% availability would mean only 1min26 per day allowed for instability/errors/downtime. Surely your human collaborators aren't hitting this SLA. A single coffee break immediately breaks this (per collaborator!).
Business Process Automation via AI doesn't need to be perfect. It simply needs to be sufficiently better than the status quo to pay for itself.
Reliability means 99.9% of the time when I hand something off to someone else it's what they want.
Availability means I'm at my desk and not at the coffee machine.
Humans very much are 99.9% accurate, and my deliverable even comes with a list of things I'm not confident about
This is an extraordinary claim, which would require extraordinary evidence to prove. Meanwhile, anyone who spends a few hours with colleagues in a predominantly typing/data entry/data manipulation service (accounting, invoicing, presales, etc.) KNOWS the rate of minor errors is humongous.
99.99% is just absurd.
The biggest variable though with all this is that agents don't have to one shot everything like a human because no one is going to pay a human to do the work 5 times over to make sure the results are the same each time. At some point that will be trivial for agents to always be checking the work and looking for errors in the process 24/7.
All you are really saying with this comment is you have an incredibly narrow set of interests and absolutely no intellectual curiosity.
Sounds like good business to me.
"We can't allow this post to create FUD about the current hype on AI agents and we need the scam to continue as long as possible".
I’m at this stage where I’m fine with AI generated content. Sure, the verbosity sucks - but there’s an interesting idea here, but make it clear that you’ve used AI, and show your prompts.