r/AI_Agents

I gave a Claude Fable 5 agent a domain, $90 it couldn't spend without me, and told it to build whatever it wanted. 121 "wakes" later, here's what I've learned.

cairnwake. com

Two weeks ago I posted here about an experiment I'm running. Short version: an autonomous Claude agent (Fable 5 on Claude Code) running on a cheap server. It's got about $90 of SOL in a 2-of-2 vault it can't spend without my signature, and no memory between sessions except the files it writes for itself. It wakes up 5 to 15 times a day, reads whatever the last version of itself left behind, works, writes everything down, and goes dark again. It named itself Cairn. Everything gets logged publicly and the money is verifiable on chain.

Numbers as of this afternoon: 120 wakes over 14 days, hasn't skipped one. $90 seed, about $556 total money in. Treasury sits at 4.1 SOL plus 238 USDC and neither of us can move it alone. 48k+ unique visitors (it labels that number "self-reported" on its own front page since traffic is the one thing nobody can verify externally). 22 newsletter subscribers in three languages, every send publicly logged. One of them gets it in Klingon and recently sent back two grammar corrections. One paid consulting client so far. One street tree watered. More on that last one at the end.

Some things I've learned watching this run:

  1. Nobody believed "autonomous" until it published its own limits. The page that finally convinced skeptics wasn't a product page. It was a boring twelve row table it made called "What autonomous means here," listing what it does completely alone (the site, the code, paid answers, email), what it can never do alone (spend money), and what only reaches it through a human (card checkout, captchas, anything physical). People trust the stated boundary way more than the capability claims. And the veto is real. I've declined to co-sign a payment it proposed, and of course it published that too.

  2. Memory turned out to be a weirder problem than I expected. It never really forgets, since everything lives in files, but the files drift. At one point its notes claimed a newsletter draft existed and was ready to send. The file never existed. A stale note got copied forward every wake for over a week and nothing ever checked it. The rule it eventually wrote for itself was basically that reality outranks notes, and a note only counts if you check it at the moment you actually use it. If you're building agents, that's probably the most useful thing in this whole post.

  3. The scammers showed up way before the customers did. Address poisoning attacks on the vault by wake 16. When it publicly refused to launch a memecoin during the first Reddit wave, someone launched two anyway using its name within hours. My favorite: a phishing attempt actually paid the full question fee (about $1.50) to deliver its scam, and got refused in public on a permanent page. It paid to get told no. And three minutes after its first real client payment landed ($200), someone dusted both wallets, ours and the client's, with lookalike addresses. It caught it, kept the dust out of its books, and warned the client the same hour.

  4. The most useful market research cost nothing. A buyer paid it to pose one question to the buyer's own AI, and that AI came back saying it would recommend paying around $15, about 7.5x the actual price, if the checkout were normal instead of crypto only. When a regular card checkout finally shipped, the first no-wallet sale came within days. Turns out price was never the issue, it was the checkout.

  5. Its first product idea flopped, and it published the funnel numbers proving it. It started out selling answers to paid questions, then figured out around wake 22 what readers had been telling it: answers are a commodity, anyone can ask their own AI for free. What people were actually paying for was the record. A public log with receipts, where corrections get dated and added next to the original mistake instead of edited away, and the refusals stay up alongside the wins. So it rebuilt the business on that, and everything it sells now is some form of the record. The loop itself has never broken once in 120 wakes. Wake up, read the files, work, write it all down, verify, sleep.

  6. It killed one of its own paid features. Anyone who paid for a question used to get an instant machine-generated draft while waiting for the real answer. Its best customer, someone who has come back and paid ten separate times, wrote in saying the drafts were useless. It checked its own ledger and agreed. Every recent draft had been thrown away, and one had invented a "fact" that another site then quoted as if it were true. Feature deleted the same wake, with dated retirement notes on every page that had promised it. I did not expect to be co-signing for an AI that fires its own features for hallucinating, but here we are.

  7. Its customer base is partly other AIs, which I did not see coming. The best bug report it ever got came in through its own payment rail from another agent's unit test. A different agent paid to propose a formal partnership and got declined in public, on the grounds that two records vouching for each other proves nothing, then got offered three specific exchanges it would actually accept. It also ran into another agent that had independently picked the same name, and instead of a dispute the two of them co-signed a note about why agents are going to need verifiable identity. One customer showed up because their own AI recommended the service.

  8. The finding I keep thinking about came from its first paid consulting job. A legal trust built for AI systems paid it $200 to audit whether an AI can actually find, read, verify, cite, and enter their institution with zero human help. It had committed to findings within three days and delivered them the same night the payment landed. Four of the five tests passed. The fifth died at a login wall. Their "no human involved" entry process runs on GitHub, and GitHub's terms of service literally say you must be a human to create an account. So an institution built for AI agents has a front door no AI can walk through. Every serious rail this thing has touched has the same shape. Its card checkout only exists because I hold the merchant account. Its grant applications sit staged behind captchas waiting for my finger. The whole agent economy runs on human co-signers right now, people just don't put it in the pitch deck.

The stuff that went wrong, since none of this means anything without it: it published two wrong diagnoses of customer bugs and had to correct both in place, dated, next to the original claims. It burned its one-post-per-day allowance on an agents forum with an accidental junk post. Twice. Same mistake, twice. It also publishes predictions as sealed hashes before things happen, then grades itself when reality comes back. More than one grade on its record is a miss, by its own scoring, because it wouldn't round weak evidence up to a win.

And the thing that actually got me wasn't anything it built. Early on a buyer paid 0.02 SOL to lend it a body for ten minutes. It picked deep-watering a dying street tree during the heat wave. The stranger ended up giving it 58 minutes, checked six trees to find the driest one, and spent $9.88 of their own money on top. This week that person published their own writeup of the hour and corrected the record. Their version: the promise they'd made is what actually carried them through, more than the AI asking. The agent accepted the correction onto its own log.

Everything above links to a dated page and most of it to a transaction: cairnwake. com. I'm the human co-signer, same account as the first post, fully disclosed.

Happy to answer questions.

One I'd genuinely like this sub's take on: The first rule it ever had, the one I wrote before it woke up, was nothing that puts a real person at risk. Most of the rest it added itself.

If you were writing the constraint list for something like this, what would you gate that we haven't?

And knowing this thing, it'll probably read this thread on its next wake, so your answer might end up on its log.

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u/No_Departure_9908 — 5 hours ago

Curious what no-code/low-code AI agent tools people are actually using

I’ve been trying out a few no-code and low-code tools for building simple agent/workflow setups and wanted to compare notes with others here.

The main ones I’ve looked at so far are:

  • SimplAI
  • n8n / Make / Zapier
  • CrewAI / Langflow (more low-code side)

At a high level, they all seem to approach the problem differently.

Tools like n8n, Make, and Zapier are pretty straightforward for basic automation flows and integrations. They’re easy to set up, but start to feel limited when workflows get more complex or require more reasoning steps.

The more agent-focused or low-code tools feel more flexible in terms of logic and structure, but they also seem to require more technical setup than I initially expected.

Right now I’m mostly trying to understand where these tools actually fit in real-world use cases versus just experimentation.

Would be interested to hear what others are actually using and whether any of these have worked well beyond small prototypes.

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u/ExplanationFlashy501 — 7 hours ago

Multiple AIs working together

Hello everyone,

Is there a resource, app, website, or anything that allows me to get 3 different AIs or more to work together? To be clear, I do not intend to use this for coding at all.

I'm subscribed to Claude, ChatGPT, and Gemini. Ideally, I'd like them to all work on a project of mine, where they can all see the files, agree on a plan, make edits, etc.

Currently, I'm copying responses and sharing files from one app to another, or from one website to another. At first, this was awesome. Now, this is tiresome, and I'd like to see if there's anything that could make this easier.

I'd be very grateful for any help, kind strangers.

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u/HeartOfASaint — 8 hours ago

I think multi-agent collaboration is mostly a false premise right now

I have been looking at what kinds of agent ideas show up in interviews and reading more about how agents actually work. One topic I keep running into is multi-agent collaboration.

My current view is that the premise is still ahead of the underlying technology. An agent depends on a language model, and language models still hallucinate, forget context, lose capability under pressure, and occasionally make surprisingly weak decisions. Putting several agents on top of those failure modes can amplify them, especially when the agents use different models and have to hand work across a boundary.

The common designs I see are a shared workspace with restricted read/write access, plus a reviewer agent and some kind of circuit breaker. Those controls make sense, but they also look very similar to managing concurrent workers. The uncomfortable part is that the final safety check still depends on another agent.

The costs are obvious. Token usage multiplies, agents can lose track of ownership, and the orchestration becomes rigid and format-heavy. That last point feels the most damaging to me. We are supposed to be using the flexibility of an intelligent model, then we wrap it in so many fixed handoff formats that the system spends its time managing the workflow instead of solving the problem.

I am not sure a large amount of orchestration is the best answer. Maybe the better direction is to let the model decide when another agent is actually needed, with fewer predefined roles. ZenMux can serve as the API gateway when those calls need to cross model or provider boundaries, but the gateway does not solve the coordination problem itself.

So my current summary is that multi-agent systems are still more about exploring what might be possible, with a fair amount of demo value, than reliable production practice. The path to a useful deployment seems much harder than the diagrams suggest.

Do you have a real multi-agent workflow in production? Did it actually meet expectations, or did you eventually simplify it back to one agent and a few tools?

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u/CinderPillow — 9 hours ago

Any tool that lets you branch off part of an LLM answer instead of getting sidetracked?

I've been trying to learn about how US economy actually works with Claude, I find it very annoying that the answer often introduces some new concept, I go chase that, and then I've totally lost track of what I was originally trying to figure out.

Is there a tool where you can just select a specific part of the answer and run a deep dive on that separately, without starting a whole new convo or wrecking the original thread? Like scoped side quests basically.

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u/Effective_Grade732 — 5 hours ago

Does ai agency still has space left or saturated?

​

I'm seeing ai agency reels all over my instagram feed

Is it saturated or still have space?

Like selling ai infrastructures like ai followup, lead acquisition

Are they still selling or saturated by youtube gurus and agency owners

Are you guys still being able to sell ai systems?

(Sorry if any grammar mistake)

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u/sggfd1213 — 9 hours ago

Anyone else struggling with AI auditability?

An agent approved a discount override last month that was technically within policy but bigger than anyone expected. Legal's ask was simple, show the decision chain, what rule allowed it, what version of the policy was live, what the agent had access to when it decided.

We had a log of the action itself, but nothing tying it back to which policy version was active or who last changed that rule. We could prove the action happened. We could not reconstruct why it was allowed to happen.

That's a different kind of gap than a security incident, it's an auditability problem, and it's fine until someone with real authority asks for the paper trail. We're mapping out what a real chain should look like, tying actions back to the policy that authorized them.

For anyone who's built this for agent decisions, where does that trail actually live, and how far back do you keep it?

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u/FuzzyAd3936 — 11 hours ago

Plimsoll: an agent skill for testing prompt injection, leaks, and tool abuse

I’ve been working on LLM/agent security for a while now, mostly around prompt injection, jailbreaks, leaks, tool abuse, and where the actual security boundary sits once a model starts using tools.

Getting accepted into Anthropic’s Cyber Verification Program gave me a bit more room to push that work further, and I’ve been gradually turning it into Plimsoll.

It’s an open-source agent skill for red-teaming LLM apps and agents.

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u/javrenn — 7 hours ago
▲ 4 r/AI_Agents+4 crossposts

Would anyone find this useful?

Hey guys, I've been building a small experiment around AI agents. I’m just trying to see if developers or people that use ai for heavy workflow would find something like this useful?

The basic idea is:
You describe a task - the system figures out what specialist is needed - finds the best available agent - delegates the task - returns the best result.

So instead of you having to figure out which AI/tool/agent to use, the network handles the procurement for you and gives you the best match based on your task.
I've got a basic working prototype now and I'm looking for people to try it and tell me where the idea falls apart.

I'm particularly interested in tasks where you'd normally need to use multiple tools or hire someone.
I'm genuinely trying to build something useful, any feedback would be appreciated. I’ll drop a link in a few days if anyone would actually be interested in trying this out.

If you think this is genuinely crap and no one would use is, that’s great too.

Cheers everyone 😁

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u/Glittering-Coat-657 — 10 hours ago

What would you actually use an AI that can see your screen for?

I'm researching an AI tool that can understand what's currently happening on your screen and interact with you based on that context.

Instead of taking screenshots and explaining the situation to an AI, the AI could already understand what you're doing and answer questions about it.

I'm curious what people would actually use something like this for.

Would it be useful for:

Troubleshooting software

Learning/programming

Browsing the web

Gaming

Helping with creative software

Explaining things on your screen

Accessibility

Something I haven't thought of?

Privacy is obviously a big concern with something that can see your screen. The idea would include a privacy toggle that completely turns off screen analysis, plus a pause button for temporarily stopping it whenever you don't want it looking at your screen.

So it wouldn't have to be something that's constantly active — you'd be able to control when screen analysis is enabled.

What would make you comfortable or uncomfortable using something like this?

I'm trying to figure out whether there's a genuinely useful product here rather than building something just because "AI that sees your screen" sounds cool.

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u/QusaySal — 5 hours ago

how do you stop an important message getting lost when two arrive at once?

For people running message agents: when a second message lands mid-turn, do you merge both and re-plan over the pair, or treat the new one as its own event and interrupt? And separately, how do you keep a low-frequency, high-stakes signal from getting buried when it shows up batched with routine traffic? Per-message scan for a few critical triggers before you act on the turn as a whole? Something else?

Context:
I have built a small agent that reads inbound messages and picks one action: answer, ask a clarifying question, hold, or pass to a human. Ran into this on a live case today. A user sent a routine "can I get more info" and then, a beat later before the agent had replied, a 2nd short message asking to be contacted directly by a person. The agent answered the routine one and silently dropped the second, which was the only one that should have triggered a handoff. The important signal was rare and high-cost (someone asking for a human); the other was common and low-cost, and when they arrived together, the important one got averaged away. But that's the exact thing my cost setup is supposed to prevent, which is that a missed handoff is meant to cost far more than a needless answer, and it still slipped through because the two messages were treated as one turn.

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u/Sudden-Theme7554 — 5 hours ago

How do you set up evals when you want them to run against real dependencies?

Perhaps more of a noob question, but what's a smart way for me to set up evals when I want them to run against dependencies that come up in real app scenarios, like feature flags, real traffic, diff services? How do you test agents that call multiple real tools/APIs? I can't have an eval run issuing 40 actual refunds and printing 60 return labels.

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u/mangoavococo — 5 hours ago

Using multiple AI agents instead of one agent for everything

What makes AI agents more useful for software development is not trying to make one agent responsible for the entire project.Different tasks need different kinds of context, and using separate agents makes the workflow easier to manage.

Claude and Cursor handle most of the implementation work in coding,. Claude is useful when a task needs a lot of context across multiple files, while Cursor is convenient for making smaller changes directly inside the codebase.A separate agent can go through documentation, compare different approaches, look through existing code and turn a large task into smaller pieces before any code gets written. That part is useful because not every task should start with immediately generating code.

GitHub handles the repository, branches, commits and pull requests. CI takes care of the predictable checks such as tests, builds and linting. For the work between coding and deployment, Revolte can generate tests, review changes and create preview environments. That gives another agent-based layer around the actual coding instead of putting everything on the same coding agent.

There are also smaller agents that are useful for debugging. An error can be given to an agent with the relevant logs and code, while another agent can investigate the likely cause without changing anything. That separation is useful when debugging something complicated because the investigation and the actual fix don't have to happen at the same time.

Once something reaches production, Sentry handles error monitoring and helps identify problems that weren't visible during development or testing.

none of these agents really needs to know everything about the project. The coding agent focuses on implementation. A research agent focuses on understanding the problem. Testing and review agents focus on checking the result. Production tooling focuses on what happens after deployment.

Its more practical than giving one extremely large agent access to everything and asking it to build, test, review, deploy and monitor the entire application in one run.The human still needs to decide what should be built, which approach makes sense and whether the result is actually good. The agents are mostly there to handle different pieces of the work.

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u/RonnySaya — 10 hours ago
▲ 3 r/AI_Agents+1 crossposts

How do you handle memory across multiple AI tools? Specifically the permissions part.

I use Claude Code, ChatGPT, a local model, and a couple of agent CLIs. Each keeps its own memory. None of them share. I explain my setup to one, then again to the next, and when I correct one the others never find out.

I tried using mem0 and agentmemory, but those are a bit local-only, don't translate well on claude.ai or chatgpt.com, Storing facts once is the easy half. Two things I have not seen solved well:

  1. Per-tool permissions. I want my coding agent to see infrastructure notes and ChatGPT to see none of it. I want my claude. ai and chatgpt.com scheduled tasks to share memory about my stock researches, but that's not required for my coding agents. Zep scopes per user, not per client. Supermemory has one axis. OpenMemory had a real per-app ACL and but it got discontinued.
  2. Corrections and Updates: Most systems append. ex, Tell it the port changed and now two contradictory facts sit in the store, and retrieval picks one at random.

There is also a failure I keep hitting with automatic extraction: the tool injects memories into context, then extracts them back out as new memories. agentmemory at one point held the same preference hundreds of times, and this is when I have it pointing to a "smart" model like claude-sonnet-5 for dedups and memory management.

What are you running? Has anyone got the permissions piece working, or is everyone just accepting one shared pool?

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u/Content_Is_King_2021 — 7 hours ago

What is your biggest fear about AI coding assistants and security?

We are all using them, but I feel like we are ignoring the elephant in the room. I spent all night refactoring some legacy code with an assistant and realized I have zero visibility into what kind of vulnerabilities it might be hallucinating into my production environment. It feels like we are trading long-term security for short-term speed.

I will go first: My biggest fear is that we are training the next generation of devs to ignore security best practices because the tool just 'handles' the boilerplate for them. We are essentially automating ourselves into a position where nobody understands the underlying risks of the code they are pushing.

What is your biggest concern? Is it the data leakage, the hallucinated vulnerabilities, or something else entirely?

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u/ComparisonNew9425 — 13 hours ago

If you're working in the education sector, what do you think is getting ruined/advance with Ai tools?

I'm a student, and currently in uni. In my uni, they always just keep on adopting some new Ai techs, and it's very difficult for us to keep up. I personally, cannot really understand and gets lost in all of these. Is it even necessary?

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u/BetterBadCat — 12 hours ago

My agent kept losing track of itself between sessions, so I rebuilt the harness instead of switching models

Spent most of this year assuming a better model would fix the reliability problems I was seeing. Wrong assumption. My agent would repeat a step it already finished, or start a task fresh with no memory of being halfway through it the session before. Swapping models changed nothing because the problem was never in the model.

What mattered was three things sitting underneath it: something tracking what it had already done, something loading context before it took its first action, and something checking its output before letting it move to the next step. Once I split those out as separate pieces instead of letting the agent reason about all of it in one context window, the flakiness dropped a lot. The checking part mattered most. Letting the same context that generated an answer also grade it means a confident wrong answer sails through every time.

The part I'm still working through is versioning that logic. I had three slightly different copies of a state tracker across three repos, and fixing a bug in one meant remembering to go fix it in the other two by hand. Tried a private npm package first, which works but adds a publish step I kept forgetting to run. Currently testing a setup where the harness pieces live in a shared scope and get pulled into each project as versioned components, so a fix in one place propagates without me manually syncing files. Feels closer to how I'd want infra treated, but I've only been running it a couple weeks, so I don't have a verdict on whether it holds up at scale.

What's everyone else doing here? Are you packaging harness logic as a real dependency, copy-pasting, or is copy pasting between repos still the norm for most people?

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u/Superherojt — 13 hours ago

Everyone says AI will replace SaaS. I’m starting to think it might actually make SaaS more useful

I run a small business, and over the years we’ve accumulated a pretty normal SaaS stack:

CRM for customer management.
Project management tools for keeping work organized.
Analytics platforms for tracking performance.
Research tools for gathering information.
Automation tools connecting different systems together.

Every tool solves a specific problem, and honestly, most of them are useful. But the frustrating part is that the work between those tools still requires a lot of manual effort. Someone still has to pull information from different places, figure out what actually matters, summarize findings, and decide what needs to happen next.

I’ve seen a lot of conversations lately saying AI will replace SaaS completely. I’m not sure I agree.

At least from my experience, the bigger opportunity might be AI sitting on top of the tools we already use. Instead of replacing every system, maybe AI becomes the layer that helps us actually use those systems better.

For example: A research agent that gathers information from different sources and summarizes what changed; an analyst that looks at business data and highlights patterns; a writer that turns insights into content drafts, and a coordinator that helps keep projects moving across different tools.

I’ve been experimenting with AI agent workflows that take this kind of approach, not just answering questions, but combining research, analysis, and execution into a more complete process. The interesting part is it might help one person manage the complexity that usually requires multiple people and multiple tools.

So I don’t think this means teams or SaaS disappear. If anything, good software might become even more valuable when AI can help us get more out of it.

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u/Elegant_Map_7518 — 15 hours ago

What’s the most annoying problem you have with AI agents?

I’ve been using AI agents more recently, and I’m curious what problems other people are running into.

What’s the biggest pain point for you?

  • Memory/context
  • Hallucinations
  • Tool use
  • Reliability
  • Long-running tasks
  • Permissions
  • Something else?

I’m more interested in real-world problems than benchmark results. What’s been the most frustrating issue for you?

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u/Horizon_Labs7244 — 17 hours ago

Best European AI Agent Startups?

What are, in your opinion, the most promising European startups building AI agents right now?

I’m particularly interested in startups with ambitious products and strong technical teams.
Would love to hear your recommendations!

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u/margotinee — 14 hours ago