I went through the public AI claims of 396 fintech companies. Only 143 could show an agent that actually does anything

I've been doing market research and ended up going through the public AI claims of 396 European fintech and financial services companies. Product pages, launch announcements, conference talks, press releases. I was looking for one specific thing. Is there public evidence that an AI system at this company takes a real action in production? Not drafts an email. Not suggests a next step. Not summarises a case file. Actually writes something into a system of record. Moves money, posts a ledger entry, closes a case, approves or declines a credit facility. 143 out of 396. About 36 percent. The other 64 percent are running copilots and calling them agents. To be clear this isn't a gotcha. A copilot that saves an analyst twenty minutes is a genuinely good product and I would ship it too. But the word "agent" is doing an enormous amount of work in that gap, and the two things have completely different failure modes. If your thing suggests and a human commits, your worst case is wasted time. If your thing commits and a human reviews afterwards, your worst case is a wrong irreversible action already sitting in production with other things built on top of it. The part I got stuck on: almost nobody publishes anything about what happens when the second kind gets it wrong. Tons of material on accuracy and evals. Almost nothing on "the agent did the thing, the thing was wrong, here is how we found out and what we did about it." Maybe that's just because nobody wants to publish their incidents. But it made me wonder whether the tooling for that even exists yet, or whether everyone is quietly reconciling by hand and not talking about it. If you're running agents that write to prod, how do you handle it when one gets something wrong? Genuinely curious whether this is a solved problem I'm ignorant of or whether everyone is improvising.