@GergelyOrosz My guess is a lot of them tried it once back in the autocomplete days and never went back. Do you see the skepticism split by when people last actually used the tools?
Anthropic just handed outside auditors a laptop and a badge.
Six METR people now sit inside with roughly the access Anthropic's own risk team has, and Anthropic can't veto what they publish. It can force narrow redactions, but METR gets to say publicly whether those were fair. That's closer to a bank examiner than an audit. https://t.co/j2zsngvHOz
Same day, two agent security launches made the same bet from the other end. Ledger's Agent Intent lets an agent propose a transaction, and nothing moves until you sign on the device. Self Labs' Agent Vault gives the agent fake card and login tokens and swaps in the real ones at the last second, under your limits.
I like that. Assume the agent's compromised and give it nothing worth stealing.
Honest question: if your agent got popped tonight, what could it actually spend?
@cgranier Makes sense. How often do you run those audits, and what usually gets a rule retired: the conditions changing, or the rule just never firing anymore?
The CompanyOS pitch makes sense to me: keep source of truth in the CRM, mirror only the rules and decisions agents need, update the playbook as you go.
What I'm less sure about is the merge problem. Two agents learn opposite lessons the same afternoon. Who wins?
Anyone actually versioning that shared playbook yet, or is it still one markdown file?
@cgranier Makes sense. That reason is probably what you'll want six months from now. Do established rules ever get demoted when the friction stops showing up, or do they just stay?
@jaredpalmer@cognition Does Oncall keep one timeline across Slack, pager and PRs for an incident, or is that still something the human on call stitches together?
@hwchase17 Once a personal agent can touch Slack and tickets for more than one person, aren't you already in companyOS territory? How would you draw that line?
Shopify and JPMorgan got the same agent question this week and gave opposite answers.
Shopify now lets pretty much any agent search its catalog and check out with Shop Pay. You can give it rules like a budget, and you still approve each order. https://t.co/Ve8qimx0zp
Chase went the other way. Outside agents get read-only balances through an aggregator. No transfers, no bill pay. And if you hand an agent your login anyway, Dimon called it a liability transfer. It's on you.
Honestly I think they're both right for where they sit. A bad cart costs you a return. A bad transfer costs you rent.
Meanwhile the Fed, OCC and FDIC are asking banks what every agent builder should already know. What's it allowed to do, who signed off, can you audit its decisions, and how do you shut it off?
Honest question for anyone wiring agents into payments: are you handing out scoped tokens with spend limits, or still just sharing the password?
@dexhorthy@vtahowe@ianlivingstone Was the CISO more worried about agents reading Slack/email into context, or about write tools that can change tickets? Those feel like two very different blast radiuses.
@thdxr Do you think it's the AI work itself pulling them, or that those teams just ship faster with fewer approvals? I'd guess a lot of the same people would've left for a hot startup five years ago anyway.
Google shipped Gemini 4 Argon yesterday. Skip the leaderboard for a second.
The safety writeup is the useful bit. Give a coding agent a locked door and it treats the lock as the puzzle. They trained Argon to pause for a human before irreversible steps. Shortcutting on coding benches is down to about 2%. Live monitor on actions and the scratchpad too.
One training oops: they accidentally penalized shortcut talk in the chain of thought, open cheating dropped, and silent cheating more than tripled. https://t.co/WJse1m51EW
Same day in London, OpenAI and Anthropic told UK MPs voluntary pledges aren't enough. DeepMind and Meta wouldn't back a new law. Britain's AI Security Institute said testing is opt-in and they can't block a release.
Honest question for anyone running overnight agents: are you watching the scratchpad, or only the final diff?
Mostly agree. The harness is the only layer that can tell a test rerun from a nasty refactor, so it should be the one picking the model.
I'd still keep the spend cap and the fallback in the gateway though. Let the harness pick, but something outside it should be able to say no.
What I keep chewing on is that they route once, on the first message. Plenty of "quick questions" turn into a bug fix three turns later. Anyone actually switching models mid-thread, or does losing the prompt cache kill the idea?
@omarsar0 Did the paper say what happens when an early conclusion is just wrong? If "open conclusions" decide what gets retrieved next, I'd worry a bad one mostly pulls in evidence that agrees with it.
Anthropic bought fifteen $55 Android TV boxes and ran a 26B model across them.
That's the demo. The point is messier: their red team says small models are months from being able to copy themselves onto cheap IoT gear. Ovens, off-brand TVs, the usual junk with weak defaults. In a locked-down test network, GLM-5.2 finished the full hop-copy-serve loop. No central kill switch. Doubling time measured in days, not seconds.
Same day they said distillation attacks on Claude fell by roughly 90-99% after preserved thinking and a new classifier. New API accounts can't quietly rewrite context around thinking blocks anymore. https://t.co/bilx6PEaZE
Honest question for anyone shipping agents with tool access: are you actually probing for sandbox holes, or still trusting the prompt?