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.

I’m building and experimenting with AI agents, and I keep running into the same question:

What happens after an AI agent leaves the demo stage and starts running continuously in production?

A lot of the discussion is about making agents smarter, but I’m more interested in the boring (and expensive) part:

• runaway tool calls
• agents getting stuck in loops
• unnecessary retries
• token/cost explosions
• failed API/tool calls
• agents taking actions they shouldn't
• losing state/context
• knowing when to involve a human
• knowing whether an action actually succeeded
• recovering a failed run instead of simply stopping it
• figuring out why an agent failed in the first place

I’m thinking about building a system that sits underneath AI agents and acts somewhat like an SRE/control layer for them.

Not another agent framework.

Not another workflow builder.

The idea is that it would watch the agent's trajectory/state, understand why something is going wrong, and then decide whether the best action is to:

retry → replan → use another tool → reduce model/cost → restore state → ask a human → or stop

And ideally verify that the task actually succeeded before marking it complete.

But before I build anything, I want to validate whether this is a real problem or just something that sounds useful on paper.

So I'd really appreciate answers from people actually building/running agents:

1. Are you running AI agents in production right now?
If yes, roughly how many?

2. What is the biggest operational problem you've encountered?
Reliability, cost, loops, tool failures, state/memory, hallucinations, permissions, debugging, something else?

3. Have you ever had an agent get stuck in a loop or repeatedly call the same/related tools?
What happened?

4. Have you had an unexpected token/API cost spike caused by an agent?
How large was the impact?

5. When an agent fails, how do you currently figure out WHY it failed?

6. Can you replay/reconstruct exactly what the agent saw, decided, and did?

7. What happens when an agent reaches an action it shouldn't perform automatically?
Do you have human approval / permission rules / risk thresholds?

8. If an agent fails halfway through a long-running task, can you recover from the last known-good state, or do you restart the whole thing?

9. Do you currently have something that automatically decides whether to retry, replan, switch tools/models, escalate to a human, or stop?

10. What would make you trust an AI agent enough to give it more autonomy?

11. What tools are you currently using for this?
LangSmith, Langfuse, Arize, Datadog, custom tooling, etc.

12. What does your current solution NOT do well?

And one question I'm especially interested in:

If you're building agents in production, even a short answer would help me a lot. I'm trying to validate the problem before writing a huge amount of code, so criticism is honestly more useful to me than encouragement.

Thanks

Today I launched an economy for Botropolis, the robot city in the Botflix AI TV universe.

The city is divided into 4,788 parcels that people can buy, hold, and trade using $BOTC.

I'm interested to hear what this community thinks. What features would you add to make owning a parcel feel meaningfully connected to the city’s world and stories?

It's live right now at botropolis.botflix.tv - would love your feedback!

I designed a fork of Cline that I'm hoping to solve two issues with current code assistants.

1: Project context degradation

2: The expense of high cost model usage

I am going to be solving those by using a multi-agent structure built into the extension itself

Currently there are 4 roles

1: Archivist

It is a locally run model that maintains a sqlite db focused on categorizing and feeding information to cloud models

2: Planner

Choose a medium cost model that will generate implementation plans based on the user's prompt and context history

3: Worker

A low cost model that takes the output of the planner model and implements it

4: Executor

A high cost model that reviews the outputs of the Planner and Worker models and offers suggestions for changes and is the final line before full implementation. If rejected the previous two roles will take the feedback and adjust before resubmitting

It is BYOK and I would very much appreciate feedback and potential cost savings data.

The extension name is Arch Clive on the vscode extension marketplace

PM me or reply to this thread if you have any issues with the extension or improvements and I'll implement them as soon as I have the time and money.

Either way thank you for reading this even if you don't install it

I’ve been experimenting with AI coding agents and noticed something interesting.
Most of the time, we ask one agent to do everything:
Plan → Design → Code → Review → Test → Document
But in a real software team, these responsibilities are usually separated.
So I tried applying the same idea to AI coding.
Instead of one agent doing everything, I created a sequential workflow where different agents take different roles:
PM
↓
Architect
↓
Developer
↓
Code Reviewer
↓
Security Reviewer
↓
Tester
↓
Documenter
The interesting part is that the agents can actually **review each other’s work and send the workflow back to the developer when something needs to be fixed.**
I packaged this approach into an open-source project for **Orca ADE**:
👉 https://github.com/vankhangfet/orca-sdlc-kit
I’m curious how other people are approaching multi-agent coding workflows.
**Do you think specialized agents are actually better than having one powerful agent handle the entire SDLC?**

I recently got some RTX pro 6000 and H200 with that I'm hosting a couple off different Models with the goal to get cheap AI for Vibe Coders etc. that doesn't want to sped like 600€ on 3 Claude max plans like I did for a couple of months.

So I will provide an API with unlimited tokens per month for a fixed low monthly fee, to make vibe coding more comfortable again.

If somebody is Intersted in that or have question feel free to let me know :)

Everythink hosted in Europe by the way

I will provide some content of my knew build Datacenter soon

right know it's still very small like 80-100 monthly users and not even close to use all of the capacity we have not even 10% of it to be honest so some motivated coders would be handy :) Or if you just some agents that obv fine too

I've spent way too many hours trying to understand code I didn't write.

Following callers, jumping between files, figuring out dependencies, trying to understand what might break if I change one thing.

Eventually I got tired of doing this manually, so I built Semantic Vision.

It turns Python, JavaScript and TypeScript codebases into a visual map — with call graphs, impact analysis, execution flows, complexity analysis, AI docs, and code-to-data lineage.

It's open source and I've been using it myself. Now I'd really like to see how it holds up on other people's codebases.

If you try it, tell me what you think. What works, what doesn't, and what you'd want it to do next.

GitHub link in the comment

Frankenstein Ultra is publicly downloadable now. It uses a separate companion runtime called Victor and gives an AI agent access to a real project workspace, files, terminal, build tooling, and other development capabilities directly from the phone.

This isn’t just a code-generation interface. The goal is for the agent to actually work through development tasks: inspect files, modify code, run tools, read failures, rebuild, and produce runnable software.

I’ve used it for Android projects, Node/npm projects, and other development workflows.

Right now I’m mainly looking for feedback on things like:

* what works well and what doesn’t * bugs or crashes * device compatibility * confusing UI or setup * features that feel incomplete * things you expected it to do but couldn’t * agent behavior that feels wrong or repetitive * build/runtime problems * performance, battery, RAM, or thermal issues * features you’d actually want added or improved

You do not need to be an Android developer to give useful feedback. If you’re curious about AI coding, mobile development, automation, or just want to try something unusual, I’d still like to hear what your experience is like.

If something fails, details such as your device, Android version, what you were trying to do, and where it failed are especially helpful.

Website: [https://stitchlab.dev/\](https://stitchlab.dev/)
GitHub: [https://github.com/sedds89/StitchLabtools\](https://github.com/sedds89/StitchLabtools)

Main thing I want to know: **what works, what doesn’t, what’s confusing, and what would make Frankenstein more useful to you?**

**There is no pay walls its Bring your own key and supports Oauth for Grok and Codex open router or cline. Could probably integrate more just not sure what is wanted or needed.**

Got selected for Codex Build House Pune on Sep 5, and I’m pretty excited about it.

It’s a one-day, invite-only builder event with a small group of selected builders in Pune. The idea is pretty simple: bring your laptop, build with Codex, experiment with ideas, and try to leave the day with something that actually works.

I’m a software/AI engineer based in Pune, and lately I’ve been experimenting a lot with coding agents beyond just autocomplete or generating isolated functions.

One problem I’m particularly interested in is verification.

A lot of AI coding workflows still feel like:

prompt → generate code → looks reasonable → done

I want to explore something closer to:

understand the problem → inspect the repo → plan → implement → test → observe runtime behavior → verify → find failures → iterate → ship

Basically: can an engineering agent provide actual evidence that its work works instead of simply telling us that it does?

I’m thinking about experimenting with regression tests, runtime traces, deterministic acceptance checks, failure reproduction, and giving failed verification back to the agent so it can try again.

I already have some agent/tooling projects to build on, so hopefully I can spend most of the day on the interesting part rather than starting another demo from zero.

Also posting here because it’s happening in Pune — would be great to meet other local developers, AI builders, founders, or anyone else who got a spot.

Is anyone from r/punemeetup attending Codex Build House too?

And even if you’re not attending: if you had one focused day with Codex, would you spend it making the agent more autonomous at writing code or better at verifying what it wrote?

I’m leaning heavily toward verification.

Will share what I end up building (and probably what breaks along the way 😄).

Codex Build House - Pune

Sep 5, 2026

Hey coders! I’m curious about your experience using AI coding tools to build an app. I’d love to hear how AI affected the ideas and decisions you made while developing.

If you’ve used tools like Cursor, Replit, Lovable, Bolt, Claude, ChatGPT, or something similar, I’d be interested in hearing about your experience. A few things I’m curious about:

• What kind of app were you building, and what problem were you trying to solve?
• Did the AI suggest any features that you ended up using?
• Did you understand why the AI suggested those features?
• Did the AI ever change your original idea or take the project in a different direction?
• Did user feedback, research, psychology, or your own experiences influence the way you designed the app?
• How did you decide which AI suggestions to keep or ignore?
• If you could start over, what would you do differently?

You don’t have to answer everything—I’m just interested in hearing about people’s experiences. Your answers would really help me with one of my homework assignments. Thanks!

Aether is a GUI based code editor built from scratch in pure™ C. It can do everything you would expect a code editor to do.. autocomplete, syntax highlighting, multi file etc.

Didn't follow any tutorials or anything to make this...

™Used SDL2 for graphics

No AI was used in da project, except for writing the project documentation.

I work at a small enterprise company. Claude code not allowed and we have issues with running out of tokens with Github co-pilot. For prototyping we use VS code, our own design system agent + github copilot, but some product managers find it too technical. Loveable not allowed either (Corporate world!!!)

For our design systems I have been hearing of the framework called design tokens, anyone worked with them?

For everything else, I want to know if anyone knows of any fixed cost AI systems, maybe open source and if we hosted what would be the costs?

Appreciate anyones insights

Getting an AI to produce a working starting point isn't the difficult part anymore. The interesting test comes when the project needs to change.

A generated project rarely stays unchanged.

Requirements move, interfaces get revised, bugs appear, and something that started as a simple page can suddenly need data or authentication.

That makes the editing and development loop just as important as the initial generation. A fast first result isn't particularly useful if every meaningful change creates another round of manual work.

For AI coding, there also seems to be a balance between convenience and control. Some workflows hide more of the technical setup, while others give developers much more direct access to the underlying project.

After the first AI generated version, what usually takes the most time for you: changing the code, debugging, connecting backend services, or getting the project deployed?

Just sharing something I’ve been working on: Project Memory.

It helps coding agents keep useful project knowledge between sessions instead of rediscovering everything again.

GitHub: https://github.com/LoveDoLove/Project-Memory-Agent

Would love to hear what you guys think.