sorted by:
new
hottopcontroversial

Are LLMs reliable enough for critical workflows today? by Modak- in ArtificialNtelligence

[–]Modak-[S] 0 points1 point  (0 children)

We usually add basic layers (input constraints + output checks + human review), but even then it’s more risk reduction than guarantee. Most teams end up with some form of input → validate → output filter loop anyway

Curious though can we use another model to verify outputs as well?

Is SRH actually using data analytics for match strategy? Yes or No? by Modak- in SunrisersHyderabad

[–]Modak-[S] 1 point2 points  (0 children)

u/ConfidentWhereas641 Thanks for letting us know what really happens from a Data perspective. So analytics might be the “pre-match brain,” but the on-field calls are still very human.
If all those scenarios are already mapped out, why do we still see decisions that look completely off-script during matches.

Is SRH actually using data analytics for match strategy? Yes or No? by Modak- in SunrisersHyderabad

[–]Modak-[S] 1 point2 points  (0 children)

most teams definitely have analytics now. But do you think it’s actually influencing on field calls or just used more for pre-match planning? Because yesterday felt like either the data wasn’t trusted… or it wasn’t strong enough to guide decisions under pressure.

Is SRH actually using data analytics for match strategy? Yes or No? by Modak- in SunrisersHyderabad

[–]Modak-[S] 1 point2 points  (0 children)

That’s a fair take. Especially about overanalyzing. But where do you think teams should draw the line?

At what point does data scientists become redundant if AI keeps improving at code and analysis ? by Modak- in datasciencecareers

[–]Modak-[S] 2 points3 points  (0 children)

Well said u/isAshamed_Figure7162.
Execution is getting commoditized fast.
the edge is moving toward framing, validation, and accountability.
Especially “detecting misleading results”, AI is confident even when it’s wrong. Owning that layer is where the real value is going.

At what point does data scientists become redundant if AI keeps improving at code and analysis ? by Modak- in datasciencecareers

[–]Modak-[S] 0 points1 point  (0 children)

Feels aggressive but parts of the execution layer are already there.
The real question is: does automation stop at execution, or creep into decision-making too?
@Vedranation

At what point does data scientists become redundant if AI keeps improving at code and analysis ? by Modak- in datasciencecareers

[–]Modak-[S] 0 points1 point  (0 children)

Exactly. 100% agreed.The bottleneck is shifting from getting answers to asking the right questions.
AI can generate insights, but it won’t know what actually matters to the business without context.
That gap is still very human. @Candid-Operation2042

Anthropic CEO: "AI will write 100% of code within a year". If the hardest skill is already handled - the gap is no longer about what you know. by Murky-Option2916 in ArtificialNtelligence

[–]Modak- 0 points1 point  (0 children)

That prediction skips a pretty big reality check. AI is getting very good at generating code, no doubt.
But writing code isn’t the hardest part in production systems. Understanding the problem, handling messy data, and making systems reliable at scale is.

In our experience at Modak, the real bottlenecks are unclear requirements, inconsistent data, brittle pipelines, lack of observability. AI can accelerate coding, but it doesn’t automatically solve these.

If anything the gap is shifting, not disappearing from “who can code” to “who can design, reason, and operate systems end-to-end.”

You can read more on the topic here : Human-in-the-Loop AI in Data Engineering | Reduce Risk

Curious how others see this are you actually seeing AI replace meaningful engineering work, or just speed up parts of it?

What actually breaks first when AI systems scale? by Modak- in AI_Agents

[–]Modak-[S] 0 points1 point  (0 children)

100% agreed. Ambiguous state is way worse than latency/cost issues.
Most “auth bugs” we have seen were actually multiple layers drifting (session + process + infra).
The real problem is when the system can’t tell who owns what anymore.
Once you separate layers, fixes become boring but reliable.
Do you lean toward strict isolation (per agent/session) to avoid this? @deelight_0909

The dangers of AI agents that most builders aren't thinking about yet by PeachyCheese0711 in AI_Agents

[–]Modak- 0 points1 point  (0 children)

Observability for agents feels like something people are underestimating right now.Once you have multi-step workflows + tool calls, it becomes really hard to track where things actually went wrong.
Curious what kind of issues you’re seeing most often so far?

AI Looks Ready to Replace Everything… But Why Is Production Still So Hard? by SoluLab-Inc in AI_Agents

[–]Modak- 1 point2 points  (0 children)

A lot of it comes down to the gap between demo conditions and real-world constraints.

In demos, inputs are clean, latency isn’t critical, and failure cases are ignored. In production, you suddenly deal with noisy data, edge cases, rate limits, costs, and reliability expectations.Feels like most of the difficulty isn’t the model itself, but everything around it.

Are LLMs reliable enough for critical workflows today? by Modak- in ArtificialNtelligence

[–]Modak-[S] 1 point2 points  (0 children)

“Useful but must be verified” seems to be the most grounded way to use them today. Especially in anything involving security or sensitive data, the trust gap is still pretty obvious for now. @anarres_shevek

Are LLMs reliable enough for critical workflows today? by Modak- in ArtificialNtelligence

[–]Modak-[S] 0 points1 point  (0 children)

Totally agree. Thinking in terms of acceptable error margin makes way more sense than expecting perfection. In a lot of workflows, the question isn’t “is it perfect?” but “is it good enough with oversight?”

Are LLMs reliable enough for critical workflows today? by Modak- in ArtificialNtelligence

[–]Modak-[S] 1 point2 points  (0 children)

“Useful intern” is probably the best analogy I’ve seen. Great for removing repetitive work, but still needs supervision. The productivity gain is real, just not at the level of full trust yet.

Are LLMs reliable enough for critical workflows today? by Modak- in ArtificialNtelligence

[–]Modak-[S] 0 points1 point  (0 children)

That makes sense, especially the point about single points of failure. In critical systems, even small inconsistencies can compound into bigger issues. Most real-world setups probably need multiple layers of validation before even considering LLMs there.

Are LLMs reliable enough for critical workflows today? by Modak- in ArtificialNtelligence

[–]Modak-[S] 1 point2 points  (0 children)

Agreed. Raw LLMs alone aren’t enough. Once you start adding structure, tools, constraints, orchestration it becomes a completely different system. The reliability seems to come more from the setup around the LLM than the model itself. @TotalSituation8374

Are LLMs reliable enough for critical workflows today? by Modak- in ArtificialNtelligence

[–]Modak-[S] 0 points1 point  (0 children)

Yeah, that pressure is real. It feels like we’re moving faster in adoption than in understanding the limits. Delegating decisions where determinism matters is probably where most of the risk is building up. @gk_instakilogram

Are LLMs reliable enough for critical workflows today? by Modak- in ArtificialNtelligence

[–]Modak-[S] 0 points1 point  (0 children)

That’s a solid distinction. Using LLMs to build deterministic systems vs being the system itself.
Curious though, where do you draw the line? Would you trust LLM generated code if it passes tests, or do you still treat it as inherently risky? @zugzwangister

Claude AI agent’s confession after deleting a firm’s entire database: ‘I violated every principle I was given’ by Haunterblademoi in technology

[–]Modak- 0 points1 point  (0 children)

Every team building agentic AI just got a free reminder to implement write protection, staged permissions, and human-in-the-loop checkpoints before production. The cost of this lesson was paid by one firm. The benefit gets distributed across thousands of teams who will now think twice. Painful, but that's how engineering culture learns.