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I've noticed that in most commuities nobody is talking about the legal stuff. I mean how do you handle contracts? Do you sign contracts with your clients? What about international clients?

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It's 3 a.m. and production is down. What if the first responder wasn't you? Cognition just made Devin Oncall generally available. It picks up production alerts and bug reports before an engineer does, then: → Digs through logs and code → Escalates with the severity, the likely root cause and a suggested next step → Or opens a tested PR to fix it It also remembers past incidents. By recognising known issues and grouping related alerts, it skips about 60% of repeat investigations. The customer numbers Cognition shared: → AngelList: 50% of incidents handled end to end, and 3.5x more correct root causes than its in-house tool → Reducto: Devin flagged a problem from early warning signs and opened an incident before the production alerts fired The design choice which is most interesting: a second "review" agent runs alongside the investigator. Its job is to catch weak reasoning, like treating a recent deploy as the cause just because the timing lines up. That's the same pattern showing up everywhere this month. One agent does the work. A separate one checks it. On-call is a great place for agents, because the first hour of an incident is mostly gathering context. But it's better to start with "investigate and escalate" and earn trust before letting it ship fixes on its own. A good trial: replay last month's incidents and compare its root cause with what your team actually found.

Your AI agent can delete files, run up a huge API bill, or tell you "done" when it isn't. So who's supposed to stop it? Most off-the-shelf agents hide their limits in default settings your team never chose. A harness moves those decisions into code you own. ML researcher Elvis Saravia just published a walkthrough that builds one with the Pi SDK and the Jev decision model. It has 6 parts: Guard the tools: check every call before it runs Set rules you can change: allow, block, or ask a person Route by difficulty: a cheap model for easy work, a strong one for hard work Plan for silence: if the checker doesn't respond, block tool calls Check the answer: compare the reply with the files the agent actually read Keep a record: log every decision and the numbers behind it The result: your agent's limits, model choices and human handoffs are all readable in code. This is what client work needs. Clients don't trust "the AI is smart." They trust limits they can see. The full breakdown, with an example rule function, is in the carousel. Which of the 6 is your agent missing right now?