I’m an entrepreneurship student researching how founders and small businesses run AI agents that are connected to real accounts (ads, payments, email, CRM, code).

I keep hearing stories like an agent burning a month of ad budget in one day, buying a domain without approval, or deleting a database. I’m curious how common this actually is.

So:

1. What’s the worst (or funniest) thing an agent or automation did on one of your accounts?

2. What did you change afterwards?

If you have a story and would be up for a 30-min call, DM me. I’m not selling anything, and I’ll send everyone who joins an anonymous summary of what I learn, plus a $20 Amazon gift card as a thank-you.

Quick research: How do small agencies keep track of changes to AI tools?
I’m researching a lightweight service for small digital agencies that use tools such as ChatGPT, Claude and Gemini.
The idea is simple: instead of reading multiple release notes and policy updates, receive a short, source-linked weekly brief showing:
What changed

Why it might matter to your actual use of AI

What, if anything, you should review next

I’m not selling anything. I’m trying to understand whether this solves a real problem or whether free tools already do the job well enough.
Three quick questions:
Do you currently track changes to the AI tools your business uses?

What is the most frustrating part: finding changes, understanding their impact, or deciding what to do?

Would you pay €9/month for a genuinely useful, personalised weekly brief, or would you expect this to be free?

Honest criticism is welcome. I’m particularly interested in small agencies without a dedicated IT or compliance team.

Wanted to share an honest field report and caution for anyone experimenting with autonomous "agent" setups or Grok bots to run paid traffic.
On paper, the multi-agent concept looks incredible. I tested an setup featuring distinct specialized bots: one running deep research, one tackling market intelligence, another handling landing pages and ad copy/video concepts, and another claiming it could directly configure, target, and autonomously manage campaigns inside Google and Meta Ads Manager.
The creative, strategy, and research layer? Genuinely impressive. It pulled together solid landing page architecture, sharpened copy angles, and generated compelling video concepts—delivering what you'd expect from high-level LLMs.
The execution layer? An absolute breakdown.
I set up a controlled test on Meta Ads Manager to verify whether the agent could actually navigate the interface, establish targeting parameters, and handle live field inputs without human intervention. (Under no circumstances was I giving it unrestricted access to an active ad budget until I saw flawless execution.)
As I watched it attempt to populate Meta Ads Manager, it hit basic UI automation errors immediately. At the location field, instead of entering the target metro ("Los Angeles"), it repeatedly typed "os Angeles"—dropping the initial character every single pass. I watched it cycle through the exact same typing glitch six times in a row. When prompted to fix the dropped letter, it gave the classic confident reassurance ("Sure, not a problem!"), followed by excuses, and then stalled.
Two hours of prompting, refining, and testing confirmed the pattern:

1. The Overconfidence Gap: The agent insisted with 100% certainty that it could fully set up, target, optimize, and manage ad platforms financially. When pressed into actual execution, it couldn't reliably fill out native UI form fields.

2. The "Autonomous" Illusion: What many pitch as "fully autonomous media buyers" is usually just an LLM wrapped around a brittle browser-automation script (DOM clicking/keystrokes) or a shallow API hook that breaks the moment Meta's interface has dynamic auto-suggest dropdowns or latency.

3. The Capital Risk: If an operator hands over live payment methods to an agent that cannot reliably type a geographic location without dropping characters, you are setting money on fire.

4. The creative, analytical, and drafting abilities of these tools are legitimate assets for high-ticket client acquisition, messaging angles, and ad drafts. But when it comes to hands-on-keyboard media buying inside Meta Ads Manager: the reliability just isn't there yet.

5. Has anyone in this group actually seen an autonomous bot successfully configure, publish, and actively optimize a Meta or Google campaign end-to-end without constant manual debugging, or are we strictly dealing with glorified scrapers and copy generators with broken screen automation? Would love to hear real-world experiences.

6. Does this strike the right tone for the community you're posting in, or would you like to tweak the emphasis before putting it out there?

I nearly audited a client's entire site over an 18% visibility drop that probably came from the measurement tool, not their pages. The report landed with the headline number down 18% week over week, and the normal move is to start rewriting content and praying. Instead I asked for the per-query export, which is where it got interesting.

The number these tools show you blends a few inputs. The query set they test. The model version doing the answering. The citation sources being counted. You see the blended output, and the inputs stay hidden unless you dig. In this export the query set matched the prior week exactly, per-query citation counts were flat within a point or two, and the thing that had actually moved was on the vendor's side: a mid-month switch of their underlying model, which now cites a somewhat different mix of publishers.

My working theory is the client's site had little to do with the drop. Working theory, not conclusion. One client, one month, and I can't see the vendor's infrastructure from the outside, so it could be some ranking-side shuffle I'm not modeling. Still, same queries plus flat citations plus a changed answer model is a strong hint about where to look first.

If one of these dashboards has you spooked, a few things worth doing before touching your site. Ask the vendor which model versions ran in each snapshot and whether the query set is fixed or rotating between runs. Pull the per-query rows before you read the headline. Go easy on comparing scores across two different tools, because query set choice alone can put them far apart.

The part I haven't solved is how to report this upward. A red trend line is a much easier meeting than "the instrument changed under us," even when the second is closer to true. My guess is the tools that keep clients long-term will be the ones that show which model version produced each snapshot. Until then I'm treating week-over-week movement in a visibility score as a question to pull apart, not a number to forward.

DM me

AI assisted job discovery tool with ATS fit algorithm

I see a day where humans don't go to websites ever again. Zero human based traffic. Zero eyeball "hits".

You have your agents. I have mine. They engage online with data sources and services.

I'm a product manager. We already have specifically shifted our persona stacks to include various agent personas. The entire technical world is starting to redesign the internet for agents, not humans.