𝗨𝗻𝗱𝗲𝗿 𝘁𝗵𝗲 𝗵𝗼𝗼𝗱 𝗼𝗳 𝗮 𝗱𝗲𝗽𝗹𝗼𝘆-𝗮𝗴𝗲𝗻𝘁 𝗔𝗣𝗜 𝘁𝗵𝗮𝘁 𝘄𝗼𝗿𝗸𝘀 𝗮𝗰𝗿𝗼𝘀𝘀 𝗮𝗻𝘆 𝗿𝘂𝗻𝘁𝗶𝗺𝗲 𝗲𝗻𝘃𝗶𝗿𝗼𝗻𝗺𝗲𝗻𝘁. @Neon a deep dive into how we built a single controller that deploys AI agents across the xpander.ai cloud and customer clusters - with the same DevEx everywhere. One API. Any environment. Same lifecycle. Thanks to the Neon team for featuring us in their Agent Builders series. Read the full post → https://lnkd.in/gn24dcsr #AI #AIagents #DevTools #Kubernetes #AgenticAI #DevEx
Learn from the xpander.ai team: How they unified agent deployment across every environment, a hard challenge when building agent platforms 👉 https://lnkd.in/gn24dcsr When building agents, it's tricky making deployment feel identical, e.g. whether agents run in your own Kubernetes cluster or in someone else’s cloud. The team at xpander.ai shared with us how they did it: They built a single deploy-agent API that abstracts away all the complexity. The same API call works whether you’re deploying to xpander’s managed cloud or to a customer’s self-hosted cluster. → One upload → One deploy call → One lifecycle Under the hood, it’s a multi-environment orchestrator written in Python that dynamically routes requests, streams images, and switches Kubernetes contexts on the fly. A perfect example of the kind of deep platform work that makes agent infrastructure scalable and developer-friendly.