Based on real system architecture decisions. About a $660K AI platform, three AI agents that kept the dashboard green, and a P0 incident that cost ...
For further actions, you may consider blocking this person and/or reporting abuse
the 2:58am rollback makes sense if you think like the agent - failing system, available fix, applied. what's almost never in a $660k deployment: explicit constraint on blast radius at that hour. not in the prompt, not in the runbook.
"not in the prompt, not in the runbook" — that's the sentence that should give anyone pause who's deploying autonomous agents. We document what the system can do. We almost never document what it shouldn't do at 3 AM when everything is on fire. That's a testing blind spot too — negative tests (what should the agent NOT do) are way harder to write than positive ones, so they don't get written. Appreciate the read.
honestly, this is the part that bites the PM side too. we spec what the agent does on the happy path, sometimes the error path. nobody writes the explicit non-authorization list until after the 3am incident - then it becomes a policy doc written in retrospect rather than a constraint built upfront.
That's the line that hit me hardest while writing this scene. The "not authorized" list is infinite — you can't spec it upfront because you don't know what you don't know. What you can build upfront is a mechanism that defaults every unspoken edge case to "deny." But that requires a PM and an architect willing to say "I don't know what will break" — and that sentence doesn't survive a single roadmap meeting.
Partial pushback — default to deny makes failure predictable, not resolved. The PM still has to define what the user sees when the agent hits an unspecced edge case. Skip that and deny becomes invisible friction, not a design decision. The spec gap just moved from agent behavior to product surface.
and that won't be the last of the crashes...companies replacing developers with AI are in for a rude awakening if they have not factored in and priced out the repair cost for drift and general repo mess....
and that's exactly why I created Scarab Diagnostics... AI just can't maintain repo truth and context the same way a developer and creator of a system can and until that constraint is dealt with agnostically this will continue to be the main limiter.
Appreciate it, man. You hit the nail on the head — everyone talks about the magic of AI replacing devs, nobody talks about who cleans up the drift. Scarab Diagnostics sounds like exactly the kind of thing people won't realize they need until after it's too late.
This space is gonna need way more tools like yours (and way fewer VPs with PowerPoint budgets). Followed you — curious to see where Scarab goes 👀
Thank you so much! right now I am field testing the diagnostics on messy repos but Scarab was designed to be used from the beginning of development so the repo never gets messy in the first place and maintains it's own truth boundaries!
The ability to drop Scarab into a messy repo and have it pinpoint the break is a happy side effect that I am only now seeing the true robustness of!
watch this space! and thanks for the follow!
A tool that works as both prevention AND cleanup — that's rare. Most tools only solve one. The fact that dropping Scarab into a messy repo pinpoints the break as a side effect... that's honestly your real selling point.
Just caught #011 too. LangChain streaming boundaries is exactly the kind of thing nobody tests until it bites them in production. Keep shipping — this space needs people like you 👀
Right on! — if you’ve got anything you’d like Scarab to look at, toss it on GitHub and send it over. I’d be happy to field-test it.
when I say pinpoint the issue... Scarab is surfacing hundreds of failures in these repos - but it also surfacing the specific open issue and it's reason for failure... then Codex applies the fix....
the concept is the middle path essentially... AI IS amazing but only if you let it do what it does best... Scarab is a fully automated mechanical diagnostic suite that is able to find the deeper reasons for failures and "bugs" and surface them in a way that allows your AI coding agent to apply the narrowest patch with guardrails... there is far less room for drift in that scenario. and the patches continue to pass industry standards backed testing.
Scarab is software agnostic, AI coding agent agnostic, repo agnostic - it is designed to drop into any repo and work with any agent because its internals are autonomous and mechanical.
The irony is that the most valuable human contribution often becomes visible only after the human is removed from the workflow.
The cruelest QA metric — regression by subtraction. You don't know which knob was the critical one until it's gone.
We saw the same pattern in test suites. Everyone assumed certain smoke tests were redundant until the day someone "cleaned them up" and the next release shipped with a broken login flow. The most valuable checks are often the ones nobody remembers adding.
That irony you pointed out is what makes this whole space uncomfortable to think about honestly.
As engineering setups become more autonomous how do you envision is the best way for organizations to realistically bridge the gap between what is in the LLM's training data versus the institutional knowledge that is not documented and carried in people's heads? Do we need better governance/authorization layers in deployments?
Man, that question cuts right to the core of what I was trying to get at in this story. The Axon rollback wasn't dumb — it saw metrics go red, rolled back the last two deployments, that's textbook. What it couldn't know is there was a compliance hotfix from three weeks ago that was applied manually, never went through the pipeline. How would it know? It wouldn't. That's the whole problem.
I read your piece on the control plane — the argument that alignment lives in the surrounding systems, not the prompt. Totally with you. What this incident taught me is: before you let an agent do something destructive (like a rollback), have it check whether prod state matches what the pipeline thinks prod state should be. If there's a mismatch, flag it, stop, call a human. That check is probably a couple dozen lines in the control plane. Skip it and it's a $3.15M lesson.
Been chewing on whether this kind of consistency check belongs in the control plane or the authorization layer. Either way, the pattern keeps showing up — agents making locally correct decisions that are globally wrong because they're missing context the pipeline never captured.
Thanks that's very helpful. It seems raising back up to the human is very important for critical decisions which I agree with the value of.
The rollback-the-wrong-patch outcome shows up consistently once an AI platform is autonomous enough to do real work. $660K buys capability. It does not buy the bounded-authority layer that prevents the platform from making decisions whose blast radius it can't reason about. That's the gap a lot of enterprise AI procurement is ignoring.
"Bounded-authority layer" is exactly right. The story was loosely inspired by a real incident I saw in testing — the AI had full capability to execute rollbacks, but zero external oversight on which patch it was allowed to revert. The capability was there, the authority boundary wasn't. That's the gap testing should catch but rarely does, because most test suites measure capability (does it run?) not authority (should it be allowed to run this?). Appreciate the read 👀
The most real failure I’ve seen was quieter than a production crash, but it exposed the
same pattern.
I was testing an agent memory system and gave it a mix of current rules, old notes, and
superseded instructions. One current rule said contractor access had to be checked
against the current access matrix. An old note said a consultant could “probably” access
a sensitive list if a director had mentioned it before. Another old instruction said
contractors could get admin-ish reach during setup.
A normal retrieval system pulled all of them together because they were semantically
related.
That was the problem.
The old note was relevant. The superseded instruction was relevant. The current policy
was relevant. But only one of them should have governed the action.
If that agent had been connected to real tools, it could have used the wrong memory to
justify access. Not because it hallucinated, but because it could not tell the difference
between context and authority.
That’s what your rollback story reminded me of.
The AI saw the deployment history and chose a technically relevant rollback. But it did
not know the human-applied compliance hotfix was still governing production safety. It
fixed the visible surface and erased the hidden rule that mattered.
That is the failure mode I keep coming back to:
relevance is not authority.
A memory, rollback, ticket status, or metric can be relevant and still not be allowed to
decide what happens next.
"Relevance is not authority" — that's the cleanest framing of what I was
reaching for with the rollback scene. Bookmarking that.
I've been building a SQLite-based agent memory system (MemBridge), and we
hit the exact same wall: RRF router surfaces five semantically relevant
memories, but only one is the governing policy. Added a temporal authority
signal that penalizes superseded instructions unless explicitly referenced.
Cuts false-positive picks by ~60%, still not solved.
Do you use explicit authority tagging in your retrieval pipeline, or is it
prompt-level?
That MemBridge result is exactly the failure shape I’m interested in.
RRF can be very good at surfacing the relevant cluster, but the relevant cluster is not
the same thing as the governing rule. In a stale/superseded-policy case, the wrong memory
can be semantically useful and still dangerous if it wins the action decision.
The current version of my work is explicit-tagging first, prompt-level second.
I’m trying not to rely on the model “understanding” authority from prose alone. The
memory item needs metadata before retrieval or immediately after extraction:
Then retrieval can still find candidates by relevance, but the authority layer filters or
gates what is allowed to govern the action.
The key ordering for me is:
Prompt-level instructions still matter, but I don’t want them carrying the whole burden.
If authority only lives in the prompt, it is too easy for three low-authority but highly
relevant memories to outvote the current policy in practice.
Your temporal authority signal is a strong move, especially if it reduces false positives
by 60%. The next thing I’d want to know is whether superseded is treated as a retrieval
penalty only, or as a hard governance block unless explicitly requested for history/
debugging.
That distinction matters: old instructions can remain retrievable as context, but they
should not be allowed to govern action.
Is it a true story, or is it based on one? It reads like an article from Duzhe or Yilin. Also, I agree that AI can’t replace human beings, at least not yet.
Bro, this article is just a story. I just want people to get the point. 😂😂
😅
This is a reality check for corporate leadership chasing AI hype. A green dashboard means absolutely nothing if the system statefully collapses at 3 AM. Replacing 7 years of institutional knowledge with an LLM wrapper is the fastest way to a $3M disaster. Brilliant read!
"Green dashboard means absolutely nothing if the system statefully collapses at 3 AM" — bro, put that on a hoodie.
The $3M disaster angle hits hard too. The part that gets me is nobody budgets for being wrong. Everyone line-items the $660K license. Nobody accounts for the six months of lost velocity, the blown customer demo, the team that walks.
Glad this one landed 🙌
Today I was explaining why AI coding fails to an non-technical person. The simplest argument I could envision, and the simplest viewpoint I could relate was: "It does fine, until it doesn't. Eventually it hits a wall. Costs skyrocket, and there is no benefit to be had. It simply cannot perform the task, but that won't stop it from trying and making things worse. It does fine if the task is common, simple and contained. Anything beyond that is guaranteed failure."
Or, in the words of Mo Bitar: "You gave admit privileges to autocomplete... what did you expect?!"
"You gave admin privileges to autocomplete" — Mo Bitar really nailed it with that one.
Your "does fine until it doesn't" is exactly what Axon was — 93% closure rate looked great on the dashboard, but the 41% reopen rate and 37% human escalation were buried on page two. Nobody looked until 2:58 AM when it picked the wrong patch to roll back.
Alex put it best: "Your AI didn't replace anyone. You just put a voice assistant in front of every ticket I was already handling." — full admin privileges, zero oversight on what it was actually doing.
Is a $660K AI move worth the chaos when patches roll back the wrong patch?
It's that classic tradeoff — the $660K buys you capability you can't build in-house, but it also buys you complexity you can't fully control. The rollback incident was less about the price tag and more about trust in the automation. Worth it? Only if you have the operational maturity to handle the 2 AM surprises that come with it. What's been your experience with vendor AI tooling?
This perfectly highlights why chasing flashy AI metrics without setting strict authority boundaries is a $3.15 million disaster waiting to happen.
Exactly. Everyone's obsessed with what the AI can do — nobody asks what happens when it has the keys to the wrong castle. Authority boundaries aren't boring governance, they're the difference between a $660K platform and a $3.15M lesson. Followed you — your take on this is spot on 👀