Hey HN - we're Tarush, Sidhant, and Shashij from Cekura (https://www.cekura.ai). We've been running voice agent simulation for 1.5 years, and recently extended the same infrastructure to chat. Teams use Cekura to simulate real user conversations, stress-test prompts and LLM behavior, and catch regressions before they hit production.The core problem: you can't manually QA an AI agent. When you ship a new prompt, swap a model, or add a tool, how do you know the agent still behaves correctly across the thousands of ways users might interact with it?
_id: '7b0c3a06-7de7-4f44-963e-656a050fbff8'
。关于这个话题，搜狗输入法2026提供了深入分析
16:01, 27 февраля 2026Бывший СССР
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而OpenClaw采用是“截图—模型推理—坐标计算—动作模拟”的视觉闭环。每一次操作都要经过完整的感知与决策流程，单步耗时往往在15至30秒之间。例如一个“登录系统并导出报表”的简单任务，集成式Agent可能5秒完成，OpenClaw却需要3至5分钟的反复尝试。效率差距由此拉开。。爱思助手下载最新版本对此有专业解读