u/Old-Roof709

we scan all our running containers weekly. most come back clean, no critical CVEs.

but some of those images haven't been rebuilt in six months. the scanner does catch new CVEs in old images  Trivy and Grype pull from a continuously updated vulnerability database, so a CVE published last week will show up against a six-month-old image in this week's scan. detection isn't the problem.

the problem is remediation. scanner fires, ticket opens, nobody rebuilds because the service hasn't changed and devs don't want to touch something stable. so the CVE sits open, aging, with no clear owner pushing the rebuild.

security wants a policy: "no image older than 30 days in prod." devs say that's impossible because some services genuinely don't change that often and a forced rebuild just to rotate the base image breaks their release process.

what we need is a way to rebase on a patched base image without triggering a full app rebuild and re-test cycle every month. right now those are coupled and neither side wants to own the cost.

how are you enforcing image freshness without forcing useless rebuilds? 

Been messing with some ai agent stuff for our support queue. the idea is it suggests fixes for common tickets and a human approves before it runs anything. sounds smart on paper but in practice im not fully convinced yet. right now its in simulation mode where it reviews tickets, pulls context, and proposes full resolutions.

most of the time it’s fine for simple stuff like password resets, access issues, and basic troubleshooting. but once things get a bit unclear or the ticket is poorly written, it starts making assumptions that don’t always hold up. we’ve seen a pretty consistent split where the majority of suggestions are usable, but there’s still a small chunk where it either overreaches or picks the wrong path from similar past cases. nothing catastrophic in testing, but enough to make you pause before letting it execute anything for real.

had one case where a user just said “can’t access system” and it jumped straight to suggesting a full account reset and permission rebuild, which would’ve been way too aggressive for the actual issue. on paper the system looks good because the success rate is high in controlled tests, but real tickets are messy and unpredictable, and that’s where the gaps show up.

how do you decide when something like this is safe enough to move from simulation to real execution without humans approving every step?

Running a small ecommerce side gig and my orders, shipping labels, customer messages are spread across ebay, shopify, some google sheet i forgot about and quickbooks.

I’m constantly bouncing between platforms just to figure out basic things like whether a customer paid twice, or to track refunds and orders properly.. At the end of the month it turns into a mess, and year end accounting is starting to look bad. How do you guys keep this straight without losing your mind?

Been messing with some ai agent stuff for our support queue. the idea is it suggests fixes for common tickets and a human approves before it runs anything. sounds smart on paper but in practice im not fully convinced yet. right now its in simulation mode where it reviews tickets, pulls context, and proposes full resolutions.

most of the time it’s fine for simple stuff like password resets, access issues, and basic troubleshooting. but once things get a bit unclear or the ticket is poorly written, it starts making assumptions that don’t always hold up. we’ve seen a pretty consistent split where the majority of suggestions are usable, but there’s still a small chunk where it either overreaches or picks the wrong path from similar past cases. nothing catastrophic in testing, but enough to make you pause before letting it execute anything for real.

had one case where a user just said “can’t access system” and it jumped straight to suggesting a full account reset and permission rebuild, which would’ve been way too aggressive for the actual issue. on paper the system looks good because the success rate is high in controlled tests, but real tickets are messy and unpredictable, and that’s where the gaps show up.

how do you decide when something like this is safe enough to move from simulation to real execution without humans approving every step?

So we automated the first layer of triage. Great. Issues get categorized, priority scored, context pulled, everything feeds into our queue perfectly sorted.

Then a human has to look at it.

And somehow we're back to square one. Ticket sits for hours because nobody claimed it. When someone finally picks it up they ask questions that were literally answered in the automated summary. Or they reopen it because they missed the context the first time. Or they escalate it wrong because they didnt read past the category tag.

We built all this logic to get tickets ready for handoff. Detailed summaries, suggested next steps, previous attempts already documented. Should be smooth right.

But there's this gap between automated system did its job perfectly and human using what the system gave them.And nobody wants to own that gap.

Management says the automation is working. Support says they don't have time to read everything. Tickets get slower response times than before we automated anything because now there's an extra step of a human forgetting to look at the notes.

Has anyone solved this or are we all just pretending automation fixes itself once a person touches it.

Rolled out this shiny ai ticketing system six months back thinking it would auto categorize tickets, suggest fixes, maybe even resolve the easy stuff without me touching it. sounds great right. users love self service. tickets drop 30 percent. pure bliss.

reality hits like a truck. ai confidently dumps password reset tickets into hardware queue because someone typed laptop once. suggests rebooting the server for a vpn timeout because correlation equals causation apparently. users now submit five tickets for the same issue because the ai replies with some generic platitude and closes it as resolved before they notice nothing changed.

spent half of yesterday manually reassigning 40 tickets it butchered while it proudly reports 98 percent accuracy in the dashboard. yeah accurate if you ignore the 20 percent that land in the wrong department and the 15 percent it just ignores entirely. leadership sees the pretty metrics and asks why slas arent improving. i explain the ai is basically a toddler playing support engineer and they nod like thats normal.

self deprecating part: i keep tweaking the training data like its going to suddenly get smarter. spoiler it doesnt. now im the guy defending why we spent good money on glorified autocomplete.

what ai ticket disasters have you all survived, any setups that actually work without turning your queue into chaos or did you just rip it out and go back to manual?

So we finally got an AI ticketing system to improve response times. You know what it did? It made everything infinitely worse.

Before: customers emailed support, someone read it, someone responded.

Now: customer submits ticket, AI categorizes it into the wrong queue, ticket sits for three days, customer gets a bot-generated response that doesnt address their actual problem, customer has to submit another ticket, repeat until they just give up and leave a one star review.

The system cost us 12k to implement. We saved maybe 2 hours per week on categorization. We lost 8 customers in the first month alone.

My manager keeps pointing to the dashboard showing average response time down by 40%. Cool. Doesnt mean anything when customers are getting answers to questions they didnt ask.

So here's my question: is this just how it goes with ticketing automation or are we using it wrong, like is there actually a version of this that doesnt make your customers want to burn the whole company down or are we all just collectively pretending this is fine.