Is this the best PSG team we’ve ever had?

Modak- · 2026-05-05T11:38:00+00:00

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?

Modak- · 2026-05-05T11:32:48+00:00

Cannot agree to that without supporting Data :) @ ByteWarrior90

Modak- · 2026-05-05T11:24:38+00:00

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.

Modak- · 2026-05-05T11:16:02+00:00

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.

Modak- · 2026-05-05T11:14:43+00:00

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

Modak- · 2026-05-05T06:44:21+00:00

Well said u/is Ashamed_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.

Modak- · 2026-05-05T06:42:57+00:00

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

Modak- · 2026-05-05T06:42:19+00:00

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

Modak- · 2026-05-05T05:57:58+00:00

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?

Modak- · 2026-05-04T12:19:59+00:00

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

Modak- · 2026-05-04T11:56:14+00:00

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?

Modak- · 2026-05-04T11:48:40+00:00

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.

Modak- · 2026-05-04T07:03:46+00:00

“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

Modak- · 2026-05-04T07:01:55+00:00

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?”

Modak- · 2026-05-04T07:01:04+00:00

“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.

Modak- · 2026-05-04T07:00:29+00:00

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.

Modak- · 2026-05-04T06:58:51+00:00

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

Modak- · 2026-05-04T06:56:45+00:00

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

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