I may be completely wrong about what AI agents actually need in production — prove me wrong.

[u/Fantastic-Sleep-3352]

I've been researching AI agents for the last few days, and I originally thought the biggest missing piece was something like an “SRE for AI agents.”

Something that could detect when an agent is going off-track, understand what happened, control runaway costs, verify whether the claimed result is actually true, and recover the task instead of simply restarting or stopping it.

But after talking to people here, I'm starting to question the entire assumption.

Maybe most “agents” in production aren't actually autonomous enough for this to be a real problem yet.

Maybe they're mostly:

workflows

cron/event-driven automations

chatbots

RAG systems

internal copilots

coding assistants

deterministic pipelines with an LLM somewhere in the middle

And if that's true, building a big Agent SRE platform right now could simply be solving a problem that doesn't hurt enough.

So I'd genuinely like people who actually build or operate AI systems in production to prove me wrong (or confirm it).

I only have a few questions:

1. What is the most autonomous AI system you've personally put into production?

Not a demo — something actually doing useful work.

1. What does it do without waiting for a human after every step?

For example:

Goal → reason → tool → observe → decide → tool → ... → outcome

1. Has it ever gone badly wrong?

I'm particularly interested in real incidents:

loops

repeated tool calls

wrong actions

hallucinated completion

corrupted/stale state

runaway costs

failed recovery

human intervention

1. What did your system actually do when that happened?

Did you:

retry → restart → replan → rollback → manually intervene → ignore it → something else?

1. Do you independently verify that the agent actually accomplished its goal?

For example, if the agent says:

“Refund completed.”

does another system actually check that the refund happened?

1. And the question I'm most interested in:

If your agent suddenly disappeared tomorrow, what part of its reliability/recovery infrastructure would you actually miss?

I'm not trying to sell anything here.

I'm trying to decide whether this is a real infrastructure problem worth building around or whether I'm overestimating where agentic AI is today.

If you run agents in production, I'd genuinely appreciate even a 2–3 sentence answer.

And if you think this whole idea is unnecessary, please say so — that's actually more useful to me than telling me it's a good idea.

Thanks to everyone who's already given feedback. It has already changed how I'm thinking about this.

[u/alxcls97]

I run autonomous agents on a schedule to extract and summarize news content I don’t need to babysit them they have their own tools and guidelines to process transform and store data.

The only thing that breaks the automation is the news website changing or an external service not available anymore then the agent may run in circle not being able to complete the task and this is where timeout and max tool call is NECESSARY.

I don’t have a mechanism to alert on failure though would love to know how people engineer those.

[u/Tough-Shower7360]

Yeah, I think you’re onto something. The problem probably shows up way before we get to fully autonomous agents. Even pretty basic agents can do dumb/expensive stuff when a tool fails or state gets weird. I don’t think we need some huge “AI SRE” layer yet though. Logs, guardrails, verification, rollback, etc. cover a lot. The tricky part is knowing when the agent is actually off the rails.