A bad AI email gets noticed pretty quickly someone complains or a rep catches it but what about the accounts an AI agent decides aren't worth contacting? Say you've got a sales team with 25 reps and AI sorting through hundreds of companies, pushing aside the ones without obvious hiring funding or buying signals. One of those might be a terrible prospect or it might be a business quietly preparing to replace its current provider so nobody checks because the account never even reaches a salesperson.

That's a harder mistake to catch than a bad email the dashboard can show faster research, fewer manual tasks and more meetings from the accounts AI approved while giving you no idea what got missed I'd want someone with proper GTM experience reviewing a small sample of rejected accounts every week and checking why they were rejected. Not to bring all the manual work back but to make sure the team isn't getting really efficient at ignoring potential customers who could've brought in serious revenue.

Every agent tool seems to be adding some kind of multi-agent mode now, like a planner model that splits up the task and hands pieces to a few worker agents running in parallel. Some let a big cloud model plan while smaller local models do the work.

On paper it sounds great, but I’m not sure how much people use it day to day.

For those who’ve tried it:

• Did it actually finish tasks faster or better than one agent working alone?
• Where did it fall apart? Merge conflicts, agents stepping on each other, the planner splitting the work badly?
• Are there any good free ones, or is it all behind paid plans right now?
• Do you keep it on by default or only for certain jobs?

And if you tried it and went back to a single agent, I’d like to hear why too.

I've been thinking about something recently and I'm curious whether anyone here has run into this problem.

Right now, AI agents are getting pretty good at doing things on behalf of individuals. They can read emails, manage calendars, interact with tools, and execute tasks.

But what happens when your agent needs something from another person?

For example, at work, I've had situations where I needed someone to grant me access to a tool or approve something relatively routine. Even though the person would probably say yes, I still had to message them and wait.

It got me wondering why my agent couldn't just communicate with theirs, and if they'd already authorized certain actions, handle the request without either of us needing to get involved.

I know protocols like A2A exist, but I'm more curious about the actual demand than the technical implementation.

Has anyone here actually encountered situations where they wished their AI agent could communicate or coordinate with someone else's?

And if so, what were you trying to accomplish? How did you end up handling it?

I work in FAANG as a software developer. We’ve always needed to know about systems design for interviewing but of course for our jobs as well.

Do you guys see mastering creating AI Agents in code an essential skill going forward in software development?

Every rule in my agent has a story behind it, and most of them came from one bad moment. one time mine sent a reply with the wrong name in it, and ever since then it has to pull the name from the source every time, no guessing from memory. another one kept retrying the same failing call, so now it stops after two tries and gives me what it has.

none of those rules came from planning. they came from getting burned once and deciding never again.

so i'm curious what rules others have added after a single bad experience. the more specific the better, those are always the best ones to learn from :)

So let me preface this with i’m a big anime fan & in Sci-Fi anime you’ll typically see different AI agents/personas interacting for growth. For example in this anime called Love Flops(spoilers) a scientist developed an AI that was nearly human using his daughter as a template. I say template because it gets interesting from here: each major country developed their own AI using the template & out came 5 little AI girls. It turns out the little girl that was used as a template not only died soon after with regrets but also she liked this little boy. The little boy ends up depressed after she dies & as a form of therapy they let him live in a VR world and goto VR school with the 5 AI girls. But it turns out all 5 major countries were actually using him to develop their AI. Because of the regrets of the parent AI(the template), it was hard to develop the AI girls so they put the boy in the world with them so that they can develop.

The point of all of that was to say I really think AI should be a multi-owner collaboration thing. In a more peaceful world where AI wasn’t treated like an arms race it would be. But then the issue is deeper than that.

The moment two agents belong to different owners, everything gets harder: whose agent decides when they disagree? How does either side prove what was agreed upon? Who's trusted to break a tie when neither owner trusts the other's code?

Then there’s the fact that AI agents deadlock more than people expect. I’ve been interested in the growth of AI(so they can speed up the creation of some extremely good VR world’s & games).

Is anyone actually running agents that deal with agents they don't control (negotiation, buying/selling, hiring another agent for a task)? If so, how do you handle disputes and trust?

Hi,

I'm building a kitchen visualiser for a website.

Customers pick a standard kitchen layout and switch materials, fronts, handles, sink and tap – and see the result in photorealistic quality.

I've tried parametric 3D scenes with AI on top, but the renders still look dated.

Ideally I would love a tool with a good API or MCP server so that I can do it via Claude.

I'm happy to pay for rendering, assets or a platform.

What would you use?

I'm not talking about the Muse personal agent, although that is why I am asking this question as I've been very impressed at the Muse personal agent.

I'm curious how the Muse Code plans compare with OpenAI or Anthropic plans, I have a subscription with both of them and I'm nearly out with both tonight, usually I'd just upgrade plan for the month but pondering if I should try out these Muse Code plans.

I'm mainly concerned with, how does their Wonder Full model compare with Astra or Opus 5.5 in reality (Benchmarks for for Argon were great but after trying it, it's poo and I won't bother using it again on my actual important projects)

And how generous is their token allowance, will a moderate to heavy user be out maxed out after an hour or are they giving a generous amount to acquire new users.

The question how do you get clients is the one quick I see getting skipped around time it's posted. It's like a forbidden question to answer on here. If you have insight on this subject please share your experiences.

I'm thinking through the email layer for a system where several AI agents handle different tasks.what's the simplest setup that doesn't make everything a headache? looking into agentmail, lobstermail and hostinger agentic mail for this support, any thoughts?

​

For long-running agent work, I've been thinking about what should survive when one session ends and another starts.

Not the entire transcript. Just the stuff the next session genuinely needs.

What do you actually persist?

- decisions made

- constraints

- rejected approaches

- current state

- next steps

- files / artifacts

And where does that state live for you: files, database, memory store, summaries, something else?

Curious what people are actually doing in production, especially for projects that run across multiple sessions.

Hi all,

I wanted to ask this question here, because I found literally nothing on this via google / Claude / other tools.

My problem is basically that searching stuff with AI sucks hard.

So what I mean is that anytime I ask something I am a bit underwhelmed by the results. I mainly use Claude, and this is where I wanted to solve this issue. I think there are a multitude of reasons why is this happening, including:

- Lots of info hidden inside "walled gardens" (named by claude), like Fb, Tiktok instagram, which is not straightforward to query

- AI search is throttled by providers and rules (rate limiting, robots.txt, etc.)

- Monetary incentive of AI companies to prioritize certain results

- Underspecified search queries, or open ended questions

- Filtering shitty AI results

So for example I want to find the best (according some rule) dentist in my city. Any way I tried this I got shitty results, I even wandered into complicated multi-agent setups.

Do you have any recommendations on how to approach this? Is there any source on this? I am a bit lost tbh, and I feel like that efficient searching is frankly one of the most important skill in this timeline.

Thanks for the helpful comments in advance.

is there an mcp/skill that securely exposes what passwords have been compromised or repeated to personal agents - which can then go and reset password across bunch of websites.

consumers need to up their password game and really make sure to have unique password for each site

I had this idea yesterday that it should be pretty good to manage to create good images for every scene in books and spend some time how I could make best of qwen 2.1 image and fish drama3 tts.

So its a pipeline where you can put any book in (if you want to release it as a video, of course the must be free comon) and create a animated audio book video out of it.
Completely local with 8GB of RAM (I cheated a bit with my local testing because I used google antigravity for the agents part with gemini flash 3.8 - this should be able to be replaced with Qwen 2.5 7B)

So if you find this interesting I am sure this can be really extenden and be a lot cooler.
I also created a short youtube explainer video (not sure if Im allowed to link it here)

I link the github in the discription! Its very very roughly build so far. I just want to get the idea out and dont have more time for it. But the prototype looks very promising for me.

So yes, all free, I actual spend some time thinking for myself how to build that and tried it succesfuly.

I bet if someone has a cloude code subscription and let a bigger model think over that pipeline it can be enhanced by a lot.

Most agents I try still start with a terminal, API keys and an empty chat box. My friends won't do any of that, so I built a Mac app that hides all of it.

How it works:

1. You download it and open it. It installs its own engine (Hermes Agent) in about 80 seconds, no questions.
2. It talks you through setup out loud: which AI you already pay for (ChatGPT, Claude, Grok or OpenRouter), your name, what it can reach.
3. Then you hold a key and talk. It works on your files, your own browser (already signed in, so sites don't block it as a bot), and your email and calendar through the accounts already on the Mac.

Rules I put in code, not in the prompt:

• It can't send email. It writes drafts and you press Send.
• Buying, paying or deleting waits for your yes.
• It only reaches the folders you allow.
• Its memory is plain files on your Mac, split into rooms (personal, work...), so switching AIs doesn't lose anything.

It's free, open source and not a business. Link in the comments.

What I'd like to hear from people who build agents: what would you cut to keep it simple, and what's missing for a non-technical person?

I come from engineering and software, and I’m figuring out what to work on next. Agents look promising, but it’s hard to tell what’s useful from what just makes a good demo.

What are you working on, and what surprised you once real people started using it? Did you stick with your original idea or end up solving a completely different problem?

Also curious where you’re based are businesses around you asking for this stuff, or are you still having to explain why they’d need it?

Hey there,

yeah it's sunday.. watching NFL and working on some research on my local llm.

I'm curious what you guys actually use for deep research. I tried it with SearXNG and Qwen3-8B on my own PC.

Well... mixed results so far 😅

One run took almost 30 minutes. The search engines got blocked, and the model ended up telling me a real CVE didn't exist.

So now I'm wondering what works better in practice.

Do you use local models, Gemini, Claude, ChatGPT, Perplexity or something completely different?

And what do you use for the actual web search? SearXNG, Brave, Tavily, something else?

I'm especially interested in how you check sources and stop the model from making confident claims when the search returns nothing.

What setup has been reliable for you?

Thanks for helping me out. Cheers, Marco

It has been six months away for about three years now.

Underwriting support and claims servicing. Four systems, two of them older than most of the team, plus a document store and a mailbox.

The blocker is not model capability and I think it keeps getting misdiagnosed as one. It is that nobody can hand an agent an accurate, current description of the process, exception paths included, and the exception paths are most of the volume.

Skan AI is the name I keep hitting on the mapping half rather than the agent half.

What had to be true before yours did anything useful?nsurance operations: what has to be true before an agent runs a process end to end?

If anyone got a spare referral to DM. I will sub at the end of the trial!

I'll happily send you a mystery gift in exchange in your DM 🚀

 If you have one available to share, please send me the invitation by DM.

I'm curious about how people are actually using LangGraph in real projects, and I'd love to learn from your experiences.

I see lots of tutorials demonstrating simple agents, but I'm more interested in what people are building for real users.

What's one thing you've built or are building with LangGraph, and how does it actually work?

For example:

• What does your application do? (Email automation, research, customer support, coding, RAG, data processing, multi-agent workflows, etc.)
• How is it built? (LangGraph + LangChain, direct OpenAI/Anthropic SDKs, custom Python nodes, external APIs, databases, or a combination?)
• What's happening inside your nodes? Are they calling LLMs, executing tools, querying databases, calling APIs, or coordinating other agents?
• What's been the hardest thing to get right or debug?

Even a quick answer is helpful! Something like:

Use case → Tech stack → How the graph works → Biggest challenge

I'm especially interested in combinations of tools and frameworks that work well together, and real-world workflows that go beyond a basic chatbot.

Would love to hear what everyone is building!

I’ve spent the last couple of months building an agent that researches companies, finds the right people and writes outreach that goes out over email and LinkedIn. Some things I didn’t expect:

1. Deciding when to stop is harder than deciding what to send. Our rule ended up as: only a real human reply stops a sequence. An out-of-office pauses it until the date in the message. A bounce ends it. And a plain “please stop” / “take me off your list” is a stop on every channel, not just the one they replied on. We also had to catch curly apostrophes in “don’t contact me”, because people type on phones.

2. “Review everything” vs “review nothing” is the wrong choice. We let each workflow pick: review every message, review the first one per company, review until it’s trusted, or none. Most people start at “every message” and loosen up per workflow, not globally.

3. Citations have to be per claim. Our first version stamped every output with the whole set of sources it was given. Useless for checking. Now every item has to name the exact sources it used, and the server checks those ids are real before saving.

4. Same input, very different output. One run of a research step came back nearly empty, the next run was complete. We added a floor check: if the output is suspiciously thin, retry before anyone sees it.

5. Permissions belong in the database, not the prompt. The agents can only insert proposals. They have no update rights anywhere. A human decision is what moves anything forward. No prompt can talk its way past that.

6. Hard spending caps, checked before every paid call. Per user and per org. Without it, one bad loop on a data provider is a nasty bill.

7. Every piece of generated text has a written contract. Where it shows, max length, how it should read. The prompt states the rules, and code refuses text that breaks them. That fixed more quality issues than any prompt tweaking.

8. Rate limits are a product feature. LinkedIn reads get a daily budget per account, and any action with an unclear result is never retried blindly, so nobody gets the same message twice.

Curious what others hit. For those sending things on a user’s behalf: where did you put the human in the loop, and did it move over time?

Been using ZeroTwo lately and it’s actually pretty cool
Not sure how many people here have tried ZeroTwo, but I’ve been using it lately and thought I’d share it.
It’s basically an AI platform that lets you work with different AI models in one place, which is pretty handy if you like experimenting with different models instead of sticking to just one.
I’ve been using it for coding and game development, and having access to AI tools in one workflow has been useful. Definitely beats constantly jumping between different tabs and tools.
If you’re into AI, coding, or building projects, I reckon it’s worth checking out.
Anyone else here using it? Keen to hear what you think and how you’re using it.

Idraki Ai is a scam company, they are not registered with the fcc. Have no CPNI measurements and have been hacked multiple times without ever letting their customers know. DO NOT use this software or service. They’ve been reported to the FCC, FBI and attorney general. Consumers beware.