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- Hacker News
- "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.
by pmarreck - > 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.
0: https://www.cs.utexas.edu/~EWD/transcriptions/EWD06xx/EWD667...
by roxolotl - "AI agents can reason toward a goal, make dynamic decisions on the fly, and learn or improve over time"
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.
- 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.
by donatj - 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!
- 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.
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.
by advikipedia - 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.by reedf1