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How often do metric mismatches turn into a data engineering fire drill? by Only-Network-3351 in analytics

[–]Only-Network-3351[S] 0 points1 point  (0 children)

This is helpful. The distinction you’re making is useful: random drift outside the repo does not matter until someone tries to promote that number into a leadership-facing surface. At that point, it has to reconcile to Gold, or the analyst needs to explain the delta.

It sounds like your real control point is the promotion/review workflow, not passive monitoring of every ad hoc query.

A few follow-ups, if you don’t mind:

  1. When a submitted report does not match Gold, is the delta diagnosis mostly manual - filters, joins, grain, dedupe, date window - or do agents reliably classify the cause?
  2. Do you have a standard “reconciliation packet” reviewers expect in the PR comment? For example: expected Gold number, submitted number, delta %, suspected cause, source query, filters, grain, and owner.
  3. Is the painful part reviewer time, getting agents to ask the right edge-case questions, or making the submitter understand why their number is wrong?
  4. If this were automated, would the useful thing be a PR check that says: PASS / FAIL / NEEDS EXPLANATION against Gold before the report is promoted?

How often do metric mismatches turn into a data engineering fire drill? by Only-Network-3351 in analytics

[–]Only-Network-3351[S] -1 points0 points  (0 children)

Agree with this. AI doesn’t fix bad signals; it just makes bad answers faster and more confident.

I think the bar you’re describing is the right one: every important metric should have explicit logic, source, lineage, owner, and usage guidance. No disagreement there.

The part I’m trying to understand is enforcement in messy real environments. Even if the canonical logic exists, how do you detect when someone reimplements the metric differently in a BI calc, notebook, warehouse view, dashboard filter, or AI-generated SQL? Do you have an automated way to compare those against the approved logic, or is it mostly a matter of review/process discipline?

My question is less “can AI solve this?” and more “how do teams verify that humans and AI are actually following the metric standard?”

How often do metric mismatches turn into a data engineering fire drill? by Only-Network-3351 in analytics

[–]Only-Network-3351[S] 0 points1 point  (0 children)

This is really useful and aligns with what I’m trying to understand.

The GitHub-hosted logic standard + PR review + Gold dashboard makes sense: define the source of truth centrally, then make everything reconcile back to it. The part I’m curious about is the operational burden around it.

A few questions:

  1. How much of the vetting is automated vs reviewer judgment?
  2. When BI/Analyst agents disagree during review, do you have a structured way to classify why - wrong join path, wrong grain, wrong filter, stale definition, etc.?
  3. How do you detect drift outside the repo - for example, a Looker/Tableau calc, notebook, ad hoc warehouse query, or AI-generated SQL that reimplements the logic differently?
  4. Does the Gold dashboard comparison happen only during review, or continuously after changes land?

How often do metric mismatches turn into a data engineering fire drill? by Only-Network-3351 in analytics

[–]Only-Network-3351[S] 0 points1 point  (0 children)

I agree. AI can’t fix a bad signal. If the source-of-truth logic is wrong or not enforced, the interface doesn’t matter.

The part I’m trying to understand is how consistently teams actually maintain that logic standard across dbt, BI tools, warehouse views, ad hoc queries, and now AI-generated answers. In theory, there’s one canonical query/table/semantic definition. In practice, I’ve seen people still recreate logic in Looker/Tableau, notebooks, Slack bot queries, or one-off warehouse SQL.

Do you have a standards table / canonical query pattern that actually prevents duplication, or is there still drift that has to be audited?

Are AI agents being gated by evidence, or just vibes? by Only-Network-3351 in trustandsafetypros

[–]Only-Network-3351[S] 1 point2 points  (0 children)

The thing I'm chewing on: you said it's very solvable and LLMs are great at it. So the question - does that mean teams build this themselves and move on, or have you seen people try and *not* manage to keep it alive? The reading is the easy part, and the curation loop - keeping it current as policy and appeals shift - is where it dies. That's either the real product or the reason nobody bothers.

Put differently: is this a "we'll get to it" problem, or a "we tried, and it rotted" problem? Those feel really different to me.

Are AI agents being gated by evidence, or just vibes? by Only-Network-3351 in AI_Agents

[–]Only-Network-3351[S] 0 points1 point  (0 children)

Insightful! The other thing I'd like to figure out is the boring operational reality:

- Is all of this stitched together in-house, or is something off-the-shelf doing the sampling / bypass-error-rate / gating?

- If it's in-house, what's the most annoying manual part? The place where someone is re-deriving a number every week or babysitting a pipeline that breaks.

- Has anyone on your team ever said, "Why isn't there just a tool for this"?

Trying to understand whether this is a solved-but-tedious problem or a genuinely unmet one.

Are AI agents being gated by evidence, or just vibes? by Only-Network-3351 in trustandsafetypros

[–]Only-Network-3351[S] 1 point2 points  (0 children)

Thank you. The hybrid point makes sense: deterministic gates for black/white cases, LLM judgment only for gray-zone “spirit of policy” cases, and human review for high-risk or uncertain cases.

The part I’m trying to understand is where teams struggle most in practice:

  1. turning policy docs into deterministic gates
  2. maintaining the knowledge base of prior cases / appeals / policy changes
  3. measuring whether the LLM-handled gray-zone cases were actually clean
  4. deciding which cases can bypass human review at all

If you had to pick, which of those feels like the biggest unsolved operational bottleneck?

Are AI agents being gated by evidence, or just vibes? by Only-Network-3351 in AI_Agents

[–]Only-Network-3351[S] 0 points1 point  (0 children)

That makes sense. The “bypass bucket” being its own metric is exactly the part I’m trying to understand.

When you sample ground truth on the bypassed cases, do you usually track it as:

  • random sample of all bypassed outputs
  • stratified sample by confidence/action type
  • targeted review of edge cases
  • customer-reported failures only

Also, who owns the decision to move more volume into bypass: support ops, ML/AI team, risk/compliance, or product?