... never met? That's the layer nobody had built, so we built it. You can build, host, deploy, evaluate, monitor and monetize AI agents . You have full lifecycle support without having to build all of that infrastructure yourself. @Kopai is now the "Cloud for AI-Agents" Under the hood, Kopai

... Achilles' heel of these all-in-one stacks. How effectively does it manage complex, long-running workflows that need to pause for human approval? Comment from Meghna Bharadwaj(@meghna_bharadwaj): @millie_masonn  Hi, Can you check out @Kopai . That is exactly what we are trying to do. Would love for some feedback so that we can improve on it Comment from Meghna Bharadwaj(@meghna_bharadwaj): @linda__jackk  Exactly, we are actually building something called @Kopai

... wanted to come back and say thank you, properly. Since launching here, Kopai crossed 150 users, and a lot of that traces directly back to this community. The upvotes mattered, but the comments mattered more: several of you asked sharp, specific questions that are already shaping what we build next. A few of you tried Kopai

... main goals of Kopai, is to make sure that the LLMs do not hallucinate. That is to stay , the human and the AI are always in Sync. Kopai

... work for a week and then suggests automations I never thought about. That feels like the next step. Comment from SWAPNANIL RAY(@swapnanil_ray): @jeremy_loomis  hey Jeremy, I took the liberty of building an agent on Kopai, by stealing your idea, haha! Check this chat out - link to chat Here's the agent I built - https://usekopai.com/marketplace... Tried comparing Linear and Clickup here. Let me know your thoughts! You can try building agents yourself too! Comment from ... ... bharadwaj): @jeremy_loomis  Not really. Disclaimer, a little self promotion here but I made a product that would solve this exact problem. Maybe if you are up for it you can go make your own AI agent on @Kopai

... Most AI chat products give you text back, maybe a code block. We wanted Kopai's agents to answer the way a good analyst would with the actual chart, table, or calculator, not a paragraph describing one. So when an agent decides a visual beats prose, it writes real render-able code and it renders live in the chat inline, not a separate canvas. It runs in a sandboxed iframe with a null origin, so the generated code ...