待翻譯：The death of AI workflow builders

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When OpenAI launched their Agent Builder in October 2025, everyone predicted it was the future of how we’d build agents. Instead of writing orchestration code, teams would drag-and-drop agentic nodes, tools, guardrails, and branches onto a canvas and connect them. Around the same time, Microsoft launched visual agent flows in Copilot Studio and Google announced Vertex Agent Designer. The interfaces all looked kind of similar and were usually built on the popular OS no-code editor React Flow. Fast forward to today, all of them are deprecated or shut down completely. Over the last few weeks, I also noticed that several startups building AI workflow builders have shut down or got acquihired: Flowise shuts down Relay.app shuts down At the time, these AI workflow builders looked compelling: Context windows were limited. Tool use and agent loops were unreliable and expensive. I remember burning a gazillion tokens on a workflow stuck in an infinite loop. More complex tasks required manually chaining prompts together. Harness engineering wasn’t a thing yet A visual canvas where you could drag-and-drop AI nodes made that complexity manageable About a month after OpenAI launched their Agent Builder, Claude Opus 4.5 got released and Claude Code took off, with Codex following soon after. Models became better at planning, tool use, and following long instructions. Coding agents became capable of creating and maintaining deterministic workflow code themselves, which became much easier than configuring a complicated visual graph in a UI. So as the above temporary model limitations disappeared, so did the need for AI workflow builders. Flowise’s sunset mentions that “developers moved on to coding agents”. I think the future lies in deterministic workflow scripts with a robust agent harness around them that jumps in whenever the script fails and needs to adapt. We use this at Kadoa to build self-healing web data pipelines: when a website or PDF layout changes, an agent investigates, fixes, and tests the extraction or transformation code. If it can’t figure it out, it escalates to a human operator. Generating deterministic workflow code got very cheap, running and maintaining it at scale in production did not. Ensuring provably correct output is still a big challenge.