I think a genuine problem right now is people are building agentic work flows and learning the hard way highly reliable agentic work flows are hard. Agents are unreliable. They are both not deterministic and not the backing APIs have pretty high error rates. Temporal has solved that pain for me and made it easy to diagnose problems.
I don’t have anything really large scale running. But big enough that it takes billions of tokens and high reliability to finish.
ive been over here using claude relatively simply as of recent, just claude code and i might enter plan mode to do some bigger like scrap together a test suite of some sort, or i just have him scripting and refactoring/reformatting stuff under my direction. i wrote my own cli tool (needed to bake in the snowflake golang driver for external browser sso propagation) and added it as a skill so he can talk to our cloud dbms when im doing analytics things but for the most part its all pretty simple. feel like my productivity is 50x but after over a year with claude ive really backed off on asking him to do insane stuff and mostly keep him churning stuff out for me in domains i know very well.
so i read all this workflow stuff that needs durability and logging and im kind of astounded how many people have their AI stuff just running on their own round the clock. i didn't realize how much of peoples day to days needed to be automated, i don't seem to find myself surrounded by much that should be automated. jira is probably the only thing i need to sit down and automate because its such a translation tax on developers just so business people can feel involved. but outside of that... guess im behind the times, but i dont know if its that. i see the big grand things people use llms for ("im creating the ultimate knowledge base" or "ive automated everything under the sun and im making 10k a week" etc) and i am feeling either too tired, not ambitious enough, or unenthused by the creative and grand ways people are working with AI. seems like everyone has their own "perfect way to use AI" but I can't seem to find the oomph to go beyond using claude as a utility anymore. a year ago (maybe more cant remember anymore its all a blur) with claude in the sonnet era i was so amazed the first thing i did was try to reverse engineer a game using ghidra. had him building test suites to verify the math was correct. we were at this for weeks. my nearby datacenter probably drained 10 lakes. that was just one of _many_ over-ambitious projects i selected because of claude that never saw a finish line.
yesterday i opened beej.us and just started reading. im young and i feel like i somehow went from 'damn this claude shit is pretty cool' to 'AI is whatever its fine' in a year. like the bell curve meme.
I've tried clever tricks to get AI produce unsupervised stuff and came back from it. The slop and loss of cognitive knowledge about what it did was uncomfortable to me... I cannot understand how you would hand off critical job to it.
They ship Helm charts so reality is somewhere between "helm deploy" and "substantial ops burden". I don't have to touch it very frequently, but that is not to say I don't have to touch it. There's occasional releases and there have been times where (probably due to my inexperience with helm) I botched an upgrade and lost some data. And I've been on this journey for years; when I first started, they didn't have a Python SDK and it was one of my (many) excuses to learn Go. But anyway to your point, yes, if you're comfortable with k8s and Helm then you shouldn't have much of a problem running hundreds of thousands of workflows; if you want to really push the throughput and optimize cost you probably need to get creative the individual services and look into cassandra (maybe? idk).
My devops coworker just shrugs, pumps out some yaml and helm and away it goes.
It really depends on your experience and tolerance for a lot of things.
Usually maintenance burden doesent start to make itself known till you get off the happy path or something breaks. Sometimes it can be a long while before that happens, sometimes it happens right away.
Other orgs have never heard of alerts or error reporting and naturally will not catch issues until they are catastrophic (for example services that crash frequently in the background go unnoticed until the crash frequency causes a catastrophic failure). In my experience a lot of issues are pretty simple such as running out of memory, CPU throttling, crashes caused by simple bugs (nil panics). If you have good observability you can catch those issues early.
For example: people rag on Ceph that their cluster somehow got into a broken state, but that really only occurs when abuse of the ceph cluster has went on long enough that the cluster finally reaches the tipping point where it is unrecoverable. If you set ceph up, follow the correct replication rules so components are spread across failure domains, and use the metrics and alerts that are distributed with ceph it is actually quite hard to break the cluster.
As best I can tell it doesn't do any batching of it's writes/reads, and it's update heavy in places rather than append (I suspect their cloud version might do some of these things)
It's pretty close to "let's make every function call serialise it's parameters/return value, go through a postgres table and several network hops"
That said it can be very useful, but it's a heavy tool that's best suited for high value/risk workflows where you're earning enough from the execution that you can afford the overhead (for example an Uber trip with several dollars of service fees is probably a good fit, unsurprisingly since it's roots are from Uber)
Self-hosting is very easy in my experience, I've done it for 2 years but management wanted to move to Temporal Cloud. They have a helm chart which just works including upgrades. This does assume you have the whole k8s shebang set up and working in your company. I never had to touch is outside upgrades which took maybe 30m including validation.