We recently spoke with 30+ startup founders and 40+ enterprise practitioners who are building and deploying agentic AI systems across industries like financial services, healthcare, cybersecurity, and developer tooling.
A few patterns emerged that might be relevant to anyone working on applied AI or automation:
- The main blockers aren’t technical. Most founders pointed to workflow integration, employee trust, and data privacy as the toughest challenges — not model performance.
- Incremental deployment beats ambition. Successful teams focus on narrow, verifiable use cases that deliver measurable ROI and build user trust before scaling autonomy.
- Enterprise adoption is uneven. Many companies have “some agents” in production, but most use them with strong human oversight. The fully autonomous cases remain rare.
- Pricing is unresolved. Hybrid models dominate; pure outcome-based pricing is uncommon due to attribution and monitoring challenges.
Infrastructure is mostly homegrown. Over half of surveyed startups build their own agentic stacks, citing limited flexibility in existing frameworks.
The article also includes detailed case studies, commentary on autonomy vs. accuracy trade-offs, and what’s next for ambient and proactive agents.
Would be interested to hear how others on HN are thinking about real-world deployment challenges — especially around trust, evaluation, and scaling agentic systems.
Lack of employee trust in these systems is caused by model (under)performance. There's a HUGE disconnect between the C-suite right now and the people on the ground using these models. Anyone who builds something with the models would tell you that they can't be trusted.
I talked to some enterprises and saw similar patterns:
1. Agentic AI systems are hard to measure and evaluate methodologically.
2. Quote from Salesforce analyst day: "it's been so easy to build a killer demo, but why has it been so hard to get agents that actually deliver the goods.”
3. Unfortunately, small errors tend to compound over time, which means most systems need a human in the loop as of 2025.
4. A lot of enterprise buyers feel the huge potential (and FOMO), yet ROI is still unclear as of 2025. MIT report "State of AI in business 2025": Despite $30–40 billion in enterprise investment into GenAI, 95% of organizations are not seeing profit and loss impact.
I've seen companies including my own pouring lots of money into AI. Outside of "replacing developers", I am genuinely curious what have people done that's actually useful?
We've got a sort of "business intelligence" AI they poured a lot of time and money into, and I don't think anyone really uses it because it makes stuff up.
I'm sure there are things. I just haven't seen them. I would love to hear concrete examples.
The cynic in me says I wouldn't want something with the error aptitude and truth telling of a small child taking any sort of important action on my behalf.
We sell some very complicated and expensive instruments. As such, making them work is quite hard. One of our biggest expenses is our engineers that go out, physically, to help customers. Company policy is that the first visit is always free. These customers can be very remote (Deep sea oil platforms, Australian outback, quite nice ski country ;) , etc). Often their issue is simple but they can also be very complex. We have phone trees, email, texts, iridium phones, etc. to talk customers through things to avoid these first visits and then hep them afterwards. So adding in AI chatbots is a natural way to help out. People don't feel quite the same 'shame' in asking really dumb questions to a chatbot that they do to a real person. So, to make these chatbots smarter, we use some of this AI mumbo-jumbo (RAG), to help them out. So far, it seems successful and the customer and engineers like the enhanced/AI manuals.
2) Making said manuals
We support 35 languages and many regulatory environments. Our instruments are all compliant with whatever version of a government agency you've got (modulo a lot of time, money, ITAR regulations). As such, making all that paper (manuals, compliance docs, contracts, etc) takes a lot of time and effort and has to pass the legal tests too. So AI is really helpful with it. Most of the work for these large stacks of paper is essentially boilerplate, but all subtly different so that literal copy-pasting doesn't get you quite that far. AI systems have been able to, last I checked, get that team about 5x faster, as it cuts out ~85% of the process and drudgery. Since these documents get hauled into courts, they can't just be blindly AI made, and a human always has to go over everything with a sharp eye still, but AI helps out there a bit too. Last lunch I had with them, they were saying that they were actually working on their burn-down charts now and not just going from panic to panic. As in, they could actually do their jobs.
What you actually need in most business cases is a 100% auditable, explainable and deterministic workflow. While AI is strictly deterministic - it is technically chaotic. Introducing this in large customer pipelines or in intensive data applications means that even if the AI only does something a bit off 99%, 99.9% or 99.99% you will see large spurious error rates in your workflow. Worst of all these will be difficult to explain - or maybe even purposely hidden, as I have seen some agents attempt to do.
Just couple of hours ago I was discussing this with a Principal Architect. He is responsible for all the finance workflows. We had just come out of product demo where the vendor showed workflows which were 100% auditable, explainable and deterministic. It required human in the loop to double check AI's work.
The feedback from the architect was that the vendor was way too cautious in using AI. Nearly all vendors he has seen so far were too cautious. He lamented that no one was fully unleashing AI. They could achieve that by allowing read/write access to confidential data like ERP/CRMs and access to internet while being fully non-deterministic. Then AI could achieve lot more.
I explained that AI being right 95% of the time is still not good enough for finance workflows but he wouldn't budge. He kept repeating that non-deterministic and remove human in the loop is the way to go. I silently promised myself to stay away from any AI projects he might be part of.
> The main blockers aren’t technical. Most founders pointed to workflow integration, employee trust, and data privacy as the toughest challenges — not model performance.
These, outside of employee resistance are technical problems. The insistence they aren’t seems to be the root of the misunderstanding around these tools. The reality is that “computers that speak English” are, at face value, incredibly impressive. But there’s nothing inherent to said systems that makes them easier to integrate with than computers which speak C. In fact I’d argue it’s harder because natural languages are significantly less precise.
Communication and integration is incredibly challenging because you’re trying to transfer states between systems. When you let “the machine carry a larger share of the burden,” as Dijkstra described of the presumed benefit of natural language programming but actual downside[0], you’re also forfeiting a large amount of control. It is for the same reason that word problems are considered more challenging than equations in math class. With natural languages the states being communicated are much less clear than with formal languages and much of the burden assumed to be transferred to the machine is returned in the form of an increase in required specificity and preciseness of which formal languages already solve for.
None of this is to say these tools aren’t useful nor that they cannot be deployed successfully. It is instead to say that the seduction of computers which speak English is more exactly that. These tools are incredibly easy to use to impress, and much more challenging to use to extract value.
"Working with AI is like being a mentor for a monkey pissing into its own mouth. Using agentic pipelines is doing the same, but now it's 5 monkeys pissing into each others' mouths in a Roman fountain kind of way."
Except it's worse than that because we'll all end up having to do it anyway, because the overall velocity of emitted working code will be faster, and productivity > all.
It took me a while to realize that the cringe AI hype bro is just wearing a tie this time. Unsubstantiated fluff with anonymous sources that wants to disguise itself as legitimate research.
Maybe at least these charts are based on real data - albeit self-reported by AI startups likely talking to their investors.
Either way it's useless unless hopping on this train is a past time of yours or you make a living taking investors - to poor to fund an OpenAI, but just rich enough to fund someone eating OpenAI's scraps - for their money.
It has been how many years of people trying to create businesses around chatgpt prompts? I think we need to bring bullying back. This is getting ridiculous.
Prediction: Non-determinism will become acceptable in areas we used to expect accuracy.
For example we will accept 'probabilistic bookkeeping' because it's cheaper than requiring ledgers to balance to the penny.
But this leeway won't be equally applied. Powerful institutions like banks will use “probabilistic models” to decide they probably don’t owe you that refund, but if they decide you owe them money, they will still hold you to every cent.
Nondeterminism for the powerful, determinism for everyone else. Yay!
I know some people in business who won’t even delegate to very competent humans who have worked closely alongside them for years. And now we have to believe that letting AI agents autonomously roam without oversight is going to be acceptable to people like that.
22 comments
[ 2.5 ms ] story [ 44.0 ms ] threadA few patterns emerged that might be relevant to anyone working on applied AI or automation:
- The main blockers aren’t technical. Most founders pointed to workflow integration, employee trust, and data privacy as the toughest challenges — not model performance.
- Incremental deployment beats ambition. Successful teams focus on narrow, verifiable use cases that deliver measurable ROI and build user trust before scaling autonomy.
- Enterprise adoption is uneven. Many companies have “some agents” in production, but most use them with strong human oversight. The fully autonomous cases remain rare.
- Pricing is unresolved. Hybrid models dominate; pure outcome-based pricing is uncommon due to attribution and monitoring challenges.
Infrastructure is mostly homegrown. Over half of surveyed startups build their own agentic stacks, citing limited flexibility in existing frameworks.
The article also includes detailed case studies, commentary on autonomy vs. accuracy trade-offs, and what’s next for ambient and proactive agents.
If you’re building in this space, the full report is free here: https://mmc.vc/research/state-of-agentic-ai-founders-edition...
Would be interested to hear how others on HN are thinking about real-world deployment challenges — especially around trust, evaluation, and scaling agentic systems.
1. Agentic AI systems are hard to measure and evaluate methodologically.
2. Quote from Salesforce analyst day: "it's been so easy to build a killer demo, but why has it been so hard to get agents that actually deliver the goods.”
3. Unfortunately, small errors tend to compound over time, which means most systems need a human in the loop as of 2025.
4. A lot of enterprise buyers feel the huge potential (and FOMO), yet ROI is still unclear as of 2025. MIT report "State of AI in business 2025": Despite $30–40 billion in enterprise investment into GenAI, 95% of organizations are not seeing profit and loss impact.
We've got a sort of "business intelligence" AI they poured a lot of time and money into, and I don't think anyone really uses it because it makes stuff up.
I'm sure there are things. I just haven't seen them. I would love to hear concrete examples.
The cynic in me says I wouldn't want something with the error aptitude and truth telling of a small child taking any sort of important action on my behalf.
1) Enhanced User guides/manuals.
We sell some very complicated and expensive instruments. As such, making them work is quite hard. One of our biggest expenses is our engineers that go out, physically, to help customers. Company policy is that the first visit is always free. These customers can be very remote (Deep sea oil platforms, Australian outback, quite nice ski country ;) , etc). Often their issue is simple but they can also be very complex. We have phone trees, email, texts, iridium phones, etc. to talk customers through things to avoid these first visits and then hep them afterwards. So adding in AI chatbots is a natural way to help out. People don't feel quite the same 'shame' in asking really dumb questions to a chatbot that they do to a real person. So, to make these chatbots smarter, we use some of this AI mumbo-jumbo (RAG), to help them out. So far, it seems successful and the customer and engineers like the enhanced/AI manuals.
2) Making said manuals
We support 35 languages and many regulatory environments. Our instruments are all compliant with whatever version of a government agency you've got (modulo a lot of time, money, ITAR regulations). As such, making all that paper (manuals, compliance docs, contracts, etc) takes a lot of time and effort and has to pass the legal tests too. So AI is really helpful with it. Most of the work for these large stacks of paper is essentially boilerplate, but all subtly different so that literal copy-pasting doesn't get you quite that far. AI systems have been able to, last I checked, get that team about 5x faster, as it cuts out ~85% of the process and drudgery. Since these documents get hauled into courts, they can't just be blindly AI made, and a human always has to go over everything with a sharp eye still, but AI helps out there a bit too. Last lunch I had with them, they were saying that they were actually working on their burn-down charts now and not just going from panic to panic. As in, they could actually do their jobs.
The feedback from the architect was that the vendor was way too cautious in using AI. Nearly all vendors he has seen so far were too cautious. He lamented that no one was fully unleashing AI. They could achieve that by allowing read/write access to confidential data like ERP/CRMs and access to internet while being fully non-deterministic. Then AI could achieve lot more.
I explained that AI being right 95% of the time is still not good enough for finance workflows but he wouldn't budge. He kept repeating that non-deterministic and remove human in the loop is the way to go. I silently promised myself to stay away from any AI projects he might be part of.
These, outside of employee resistance are technical problems. The insistence they aren’t seems to be the root of the misunderstanding around these tools. The reality is that “computers that speak English” are, at face value, incredibly impressive. But there’s nothing inherent to said systems that makes them easier to integrate with than computers which speak C. In fact I’d argue it’s harder because natural languages are significantly less precise.
Communication and integration is incredibly challenging because you’re trying to transfer states between systems. When you let “the machine carry a larger share of the burden,” as Dijkstra described of the presumed benefit of natural language programming but actual downside[0], you’re also forfeiting a large amount of control. It is for the same reason that word problems are considered more challenging than equations in math class. With natural languages the states being communicated are much less clear than with formal languages and much of the burden assumed to be transferred to the machine is returned in the form of an increase in required specificity and preciseness of which formal languages already solve for.
None of this is to say these tools aren’t useful nor that they cannot be deployed successfully. It is instead to say that the seduction of computers which speak English is more exactly that. These tools are incredibly easy to use to impress, and much more challenging to use to extract value.
0: https://www.cs.utexas.edu/~EWD/transcriptions/EWD06xx/EWD667...
Except it's worse than that because we'll all end up having to do it anyway, because the overall velocity of emitted working code will be faster, and productivity > all.
Maybe at least these charts are based on real data - albeit self-reported by AI startups likely talking to their investors.
Either way it's useless unless hopping on this train is a past time of yours or you make a living taking investors - to poor to fund an OpenAI, but just rich enough to fund someone eating OpenAI's scraps - for their money.
It has been how many years of people trying to create businesses around chatgpt prompts? I think we need to bring bullying back. This is getting ridiculous.
For example we will accept 'probabilistic bookkeeping' because it's cheaper than requiring ledgers to balance to the penny.
But this leeway won't be equally applied. Powerful institutions like banks will use “probabilistic models” to decide they probably don’t owe you that refund, but if they decide you owe them money, they will still hold you to every cent.
Nondeterminism for the powerful, determinism for everyone else. Yay!
I've yet to see an 'agentic' setup that actually learns or improves over time. There are many techniques for this, but I don't see them used.
Why do you think that is?