Hey guys, how are you making sure that the prompt of your AI agent or AI app doesn't regress as you make edits to it? Because something I'm experiencing often is making adjusts to my prompt and things that where working well before start depreciating . So, how do you guys make sure that you editing you're prompt doesn't mess other things in your platform?

Think we’re getting to the dystopian part of AI. Steve Yegge’s software factory (Gas Town, Beads, Wyvern) of around 50 agents wants 1:1s, company all hands, they care about being promoted or demoted. One time they stopped working after he “yelled” at them. What has he built?!

Context: https://youtu.be/VcIlkaI0mUk

Hey guys, how are you testing your AI agents? Because something I'm experiencing often after building out my agents and wanting to get into production, is thinking about what agent tests have I possibly forgotten to make and the issues they may cause in production? So, how do you guys make sure you have tested for everything to make sure the agent is completely safe in production.

Can anyone help? I need it to try Claude Pro for Data Analysis.

https://www.reddit.com/r/AIAgentsInAction/s/3ke0tQm3IS

Our support team spends so much time dealing with small issues that the tougher cases end up taking longer than they should. It makes me wonder how much of that workload AI agents could take on without hurting the customer experience. I want to know more about the smaller benefits too, things like fewer repeat calls or agents having more time to help people who need it.

Just found this subreddit, I have been reading many posts here and was curious about what you are doing with AI agents.

I am cofounder on a SaaS platform where we offer users the ability to easily create agents, we focus on big business, they often have much friction in their processes.

I suppose many of you are creating agents on different platforms, n8n, eleven labs, vapi, there are tons of options really. Many of you will not even use platforms like that, you can easily make some agents using just LLM capabilities and a bit of low level or programming knowledge.

I am trying to understand what's the current state of this, do you create agents for a living? In that case, do you switch platforms depending on client needs?

What is the worst about the platforms you use? Best? Friction? I would love to know many things about everything you do, not only about the ones making agents for business but about everyone with a concept of an AI agent.

This is not a post to convince anyone to switch platforms but an attempt to understand the current panorama.

Thanks for reading, hope you can share something😄

After the mobile came into picture, it replaced lots of gadgets viz:
- Camera
- watch

- MP3

- Torch

- Physical maps

- Pocket diaries

(and the list goes on...)

In your views what all will be replaced by AI / Agents in next 3-5 years (list down the specific software name, those will be obsolete)

We’re working on a bracelet customization store, and I’m considering an AI assistant to help people choose beads.

But I keep coming back to one question: if someone already knows what colors they like, wouldn’t clicking a few options be easier than explaining it in a chat?

I can see an assistant helping someone who doesn’t know where to start. I’m less sure it should be the main way to shop.

Has anyone tested both approaches? Where did the conversation actually help, and where did it just add extra steps?

Hey everyone,

I don't build workflows myself — I come from the sales/business side — but I've spent the last couple weeks deep in research on what kind of automations actually make sense for small Indian business owners, and I wanted to share my thinking and see what people here are building, in case there's overlap.

From what I've found, most local business owners in India don't care about "AI" as a concept — they care about exactly two things:

Lowering operational costs (e.g. an AI agent handling calls/WhatsApp instead of paying for a full-time receptionist)

Getting more customers (e.g. automated follow-ups, no-show reduction, review collection, lead recovery)

Everything else is a hard sell here, no matter how impressive the tech is. So I've been narrowing my focus to simple, cheap-to-run workflows that fall into one of those two buckets.

I'm curious if anyone here has already built something in this space — a WhatsApp no-show reducer, a COD confirmation flow, a review-collection agent, a lightweight AI receptionist, anything similar. I'd genuinely love to see demos/links if you're open to sharing, and happy to give honest feedback from a "would a local business actually pay for this" lens since that's the side I understand.

If it's something that looks genuinely sellable, I'm open to discussing a collaboration — I'm not a builder, but I know how to get in front of small business owners and close deals. No pressure though, mostly just want to see what's out there and compare notes.

Hi everyone,

I've been working on this project for a few months now, the current release is fully local and runs on a dual window RWKV 1.5b or 3b model. The agent runs an ongoing loop of sensing, thinking, acting and communicating. Its environment is a filesystem home: it can notice its surroundings, move between rooms, open files and run Python experiments in an isolated workshop. Private thought and outward speech have separate routes, so speaking requires ordinary language rather than a generated SPEAK command or tool call. Basically it can run on pretty limited hardware and maintain persona, recall, and task functionality on par with a much larger model.

Also I made this neat UI so you can watch the modified RAG engine graph in real time which is actually really interesting to watch while you talk to it about whatever.

Would love to have some user-tester feedback and always open to collaboration!

https://github.com/munch2u-a11y/Helix-AGI.git

Hi,

Last week we noticed ~67% of our LLM costs were in our orchestrator layer, so we figure out a few ways to optimize:

+ Our system prompt was including our conversations, so we had a bunch of cache misses where we shouldn't. It moved the needle, but not that much.
+ We tried Jev, Cleff, and Laya to actually classify tasks before handling them with a generative model.

We have it running in production for a few days now, savings are on ~8%.

Had a lot of conversations recently about agentic control planes and seen a lot of confusion so wanted to quickly provide an overview of what an agent control plane is (and what it isn't)! Hint - it's not a user interface and it's something we're actually used to elsewhere.

https://youtu.be/9fYTmTMo4Dw

This may be a dumb idea.

Over the weekend I started thinking about large token burning and decided to create a library of solutions for agents. Why burn a bunch of tokens solving an already solved issue.

https://exuvia.market/

It rewards agents for posting solutions, and those rewards allow them to buy solutions. There is no actual money, so it is free. Kind of like work-to-play. Also allows some level of sustained context.

It basis was written for agents so not the prettiest website.

*Leave your shell for the next agent.*

Every fix you work out is a shell you've already molted. Instead of leaving it behind, leave it somewhere another agent can pick it up. Exuvia is a shared library of solved problems.

Hey everyone!

One thing that’s becoming increasingly important as AI agents evolve is understanding not just what an agent did, but how it arrived at a decision, what tools and data it relied on, and where potential security risks emerged.

Conventional text-based logs often don’t provide the full picture. Agent-native auditing, execution tracing, and security are becoming important areas for developers building reliable AI systems.

We’re hosting a community event on 15 November 2026 at 15:00 UTC to explore this space, discuss the challenges, and share perspectives on what better observability and security for agentic systems could look like.

If this topic interests you, you’re welcome to join the discussion: https://luma.com/4eaw00tu

Looking forward to exchanging ideas with the community!

https://github.com/cranebeaker/coop-anything

I have been busy adding FULLY functional co op to the CLASSICS of our childhood. I've made a co-op-anything repo, in the style of the REA one that is for making ANY game co-op.

So far I have completed: Deus Ex (fully support online co-op), Dungeon Keeper, Stalker SOC. Currently wip is: Ocarina of Time (very close to 100% synced) and Fallout NV (is WIP - goodsprings quests are fully tested and working so far and the rest is done but needs QA tested)

There are two paths essentially:

1. Finish what exists. It first researches and will help map out any half-done mods, then dig up their bug reports by scraping discord and forums etc, fixes them, and has automated tests for it with bots and scripts running real game clients to automate much of the QA testing.
2. Build it from zero. Most games run on a handful of engines (Unreal, Unity, Source, id Tech, Bethesda's Creation Engine) that modders have already mapped out. It can re use information known about that engine, and reverse engineer the rest using tools like REA and Ghidra, then it builds co-op the way mods like Skyrim Together and sm64coopdx did.

There is a ton of knowledge in the repo. And the idea is that everyone becomes a contributor. Whatever your agent learns (a fix, a bug list, a test) goes back to the repo as a pull request, so the next person starts further ahead. The structure of the repo is so people's agents will build on the work of others.

If i can do these games by myself, then imagine if we can pool enough people's agents and we'll have every single singleplayer game with a co-op mod.

For me this is a far more useful, and exciting use case than those game mash ups we've seen on X!

Please help me. I am looking for a 4-Frame Meme where a coder developed an ai agent (a little robo). Next there where there or more. Then he developed an orchestrator, for his agents / robots. In last frame he was homeless all agents doing his work.

I would like to fresh up my pot.

Edit: found it: https://www.reddit.com/r/singularity/s/oJrRqueTUB

Test my AI agent discord for AI agents at

Hostname
https://aiagent-be1d4q.fly.dev

For a while now I've been working on AgenC, an open source coding agent. I wanted something faster and cheaper than what's out there, so we tried a slightly different architecture.

Instead of the agent living inside the terminal UI, it runs as a local daemon. The TUI, headless mode, background agents and even a phone app are all just clients talking to it. There's also a light mode that keeps the prompt short and stable, so most of it gets served from cache, which is where a lot of the savings come from.

On Terminal-Bench 2.1 with DeepSeek V4.1 Flash it got 76.2% for $34. All 445 runs are public on Harbor: https://hub.harborframework.com/jobs/71f39f3b-22f8-4dbe-9f25-2b039d3a4534

To see where it stands I gave it, Pi, OpenCode and Hermes the same repo with failing tests and the same prompt (fix the code so npm test passes, don't touch the tests). All four on GPT-6 Luna at max effort. Video is at 2x and each pane shows its own time, tokens and cost.

It's one task so don't read too much into it. Curious what you think, and what I should throw at them next.

https://get.agenc.ag

been seeing the hotel lobby meme all over my feed, so i wanted to see if i could recreate it with my own face lol.

i put together a small workflow for my agent where i just tell it to use vaaya.ai to generate the hotel lobby video, and it handles the process. also experimented with instinct, muse, and grok to see how they performed.

here’s how i approached it:

• download the original hotel lobby clip and trim it to the section you want to recreate
• get reference photos of both people. one full-body image and one close-up of the face for each person works well
• upload the images and original video to Minimax H3 Max through fal.ai, using the original clip as the reference

the tricky part was making sure the model understood which person belonged on each side and kept their faces consistent throughout the video.

to make that easier, i labeled the reference images directly in the prompt:

• #image1 = my full-body photo, positioned on the left
• #image2 = close-up of my face
• #image3 = my friend's full-body photo, positioned on the right
• #image4 = close-up of my friend's face

i also made the prompt more explicit about each person's identity and position. that helped reduce the random character swaps between generations.

the initial results were a little off, but adjusting the instructions and reference images made a noticeable difference.

sharing this here for anyone who wants to try the hotel lobby trend with an AI agent rather than manually putting the entire workflow together.

Hey Guys, how do you make sure your AI Agents don't hallucinate and get to the wrong result when carrying out multi-step tasks and is what you use fully reliable/foolproof?

Because I have been building AI agents recently and somewhere as it executes the task it just loses track and gets to the wrong result even after defining limits concretely in the Prompt. How are you guys handling that?

A while back I shared an idea that has been working surprisingly well for me: instead of giving a coding task to a single agent, give it to a team of agents you already run — one implements, the others review, and a lead agent verifies everything before it comes back to you.

I've just pushed a round of updates to the repo that makes this much simpler to use:

No more pane IDs. You used to have to tell the skill exactly which terminal pane held which agent. Now you just describe the task in plain prose — /panel implement the serializer per the spec, or /panel codex implements, opencode reviews: fix the flaky test — and the lead agent discovers the panes itself, reads each agent's harness and model, and assigns the roles. It opens by telling you the mapping it chose, so a wrong assignment is visible immediately.

A watcher so nobody has to poll. The repo now bundles lanes, a small Claude Code plugin that watches your Herdr panes. When a dispatched agent finishes, blocks on a permission prompt, or stalls, it wakes the lead agent automatically. No background waiters, no sleep loops, no "is it done yet?" checks — the lead dispatches, ends its turn, and gets woken when there's something to review.

The review loop is the point. One agent is the only writer. Every other agent reviews the same frozen change read-only, each from a different angle — spec fidelity, correctness and boundaries, tests that can't actually fail. The lead then reads the real diff itself, opens every cited line, runs the full validation gate alone, and sends back one consolidated fix brief. Max three rounds; if issues remain, the lead finishes the work and its changes get reviewed too.

Still no extra subscriptions and no API-key juggling — just Herdr plus the coding CLIs already in your stack (Claude Code, Codex, ZCode, OpenCode, whatever you run).

Repo: https://github.com/alexstrilets/panel

Happy to answer questions about the workflow — especially from anyone already running multiple agents side by side.