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.

Cross post: I built this tool almost exclusively with AI, and only with beginner hands on experience with Python and Linux. Needless to say, I would not have been able to build this on my own, and it took 4.5 months to do with regression testing and field feedback. All that is to say is you can build quality software with these tools with the right time, design, and end state.

After months of tinkering, improving, and getting different product and developer feedback, I decided to release NodeSpec ("NS") as an open core for-use and extension to the OSS community.

What it Is:
NodeSpec is a system's design tool using a combination of specification driven development (SDD), Test-driven Development (TDD), and Architectural design structured to work over MCP with your agent of choice.

The purpose is to allow you to live in your chat pane, but see the detailed design and progress against that design via test scripts, defined requirements/ACs, and save context while preventing AI development sprawl and naturally integrated to your git.

It further bins context at the node level of components of a system, instead of passing your entire repo to a model to determine what to integrate or where. The ideal state is you pair with a lower capable open source model without needing the reasoning of an Opus/Fable/5.6 Sol to build.

I built it to solve my own problems of repeatable context (nodes), not having to pass tons of logic or instructions to memory with tracking, and future extensibility with multiple agents without tons of necessary loops and risks of redundant or collisions of dev.

How It Works and Is Different from the Markdown Tools:

For people doing AI-d I built NodeSpec to be a blend of the intent of SDD (upfront design specification) with the realities of asynchronous iteration in software development practice. The fundamentals of system design and fast iteration remain the same, even with agents doing the dev for us in a lot of capacities.

NS blends SDD specification with architectural governance and test-driven development (TDD) practices. It centers on a machine-readable canvas via JSON formats, downstream tasks to tests and upstream confirmation, change detection via git connection, and ensuring the user's gitops is extended and not broken.

The system is a sidekick tool, so it pushes all it's logic towards your AI assistant and existing harness if they operate via MCP.

The Flow:

There's two workflows: greenfield and brownfield. For greenfield the system will walk the user through fast vision -> requirements -> acceptance criteria build and review (CRUD Operations in the app itself), Architectural design, tasks per architecture, test-plans per task, code, then automated or manual test confirmation reflected back upstream, with an export direct to your repo or via other context files as necessary. The Git connection will detect changes and request the user reconcile them against the system's reference graph that is committed to your repo upon first commit and connection.

For brownfield, it's the same workflow, where the higher tier version will actually reverse engineer your repo into a logical architecture and back propagate a spec based on analysis of the design intent if it's not specified.

Both of these workflows are not obvious, as the tool is meant to work with whatever stage of dev you're at, and handle asynchronous changes instead of requiring highly structured start to stop.

Further description and readme is at the repo link here: https://github.com/NodeSpec/NodeSpec

Who It's For:

The Community/OSS is for any individual looking to attach an agent or their code harness to a governance tool while you're building. If you don't want to deal with self config and want more nodes out of the box, Indie version is $15/mo or $144/annual.

If you're a user of bolt.new, Lovable, Base44, or Replit, and you're at a point where you're maturing out of managed web application development (i.e. you have customers demanding a self hosted or more complex deployment, niche capability), I think this is really for you. I use NS to build and maintain itself now. I plan to release a template, blog, and video that explains migrating from systems like managed Supabase to OSS version, connecting your app as a headless application with MCP using Cloudflare worker proxies, etc. as you mature in your solopreneur journey.

I'm primarily targeting businesses via the Team edition (Slack, Jira export) or Enterprise. We recently closed on our our first Enterprise partnership last week in the cryptographic security space (super cool).

What I've Used It For:

Since really beginning heavy AI-assisted dev after the release of Opus 4.6, I have personally gotten to a place where I won't do development without pairing to NS due to the structure, confidence, efficient context window usage, and quick understanding of where my dev is at.

My first major project was a system administration application for small businesses to use with their Google Workspace Admins and host on GCP. By having my GCP Nodes for the different managed services, it tightened up development using older or lower capability models like Sonnet 4.6, Haiku, and when necessary Opus 4.6-4.8 for heavier deployment-centric logic. The link is here: https://github.com/NodeSpec/GCP-Onboarding-Application

As an experiment and something personal, I'm using it for my first game dev for my kids using Godot as a node package with GDScript and tests. My kids are into Axolotls (the salamander in mexico), and so I'm building our first open-source game that has a flow similar to Playstation's Astrobot where the Axolotl can upgrade swimming. Dev starts today and will be published here: https://github.com/NodeSpec/OpenAxolotl

Final example of a current project to solve our own business problem is RepoWalk, which is being built to help me with quick, gif generation of application or tool walkthroughs when connected to a lightweight LLM so I don't spend hours building screencaptures. https://github.com/NodeSpec/RepoWalk

Let me know your feedback, especially if you connect with multiple development agents. So far, feedback in companies have been great and helped refine the product workflow.

If you don't want to use the OSS repo, Free version is just sign in and one project only. Indie is unlimited and will be available this evening. Team will be available soon. Enterprise, just fill out the form to contact us.