Scenario generation + real conversation import - Our scenario generation agent bootstraps your test suite from a description of your agent. But real users find paths no generator anticipates, so we also ingest your production conversations and automatically extract test cases from them. Your coverage evolves as your users do.Mock tool platform - Agents call tools. Running simulations against real APIs is slow and flaky. Our mock tool platform lets you define tool schemas, behavior, and return values so simulations exercise tool selection and decision-making without touching production systems.Deterministic, structured test cases - LLMs are stochastic. A CI test that passes "most of the time" is useless. Rather than free-form prompts, our evaluators are defined as structured conditional action trees: explicit conditions that trigger specific responses, with support for fixed messages when word-for-word precision matters. This means the synthetic user behaves consistently across runs - same branching logic, same inputs - so a failure is a real regression, not noise.Cekura also monitors your live agent traffic. The obvious alternative here is a tracing platform like Langfuse or LangSmith - and they're great tools for debugging individual LLM calls. But conversational agents have a different failure mode: the bug isn't in any single turn, it's in how turns relate to each other. Take a verification flow that requires name, date of birth, and phone number before proceeding - if the agent skips asking for DOB and moves on anyway, every individual turn looks fine in isolation. The failure only becomes visible when you evaluate the full session as a unit. Cekura is built around this from the ground up.
关键在于，在 Q4，「百度核心 AI 新业务」占季度总营收的比例为 43%——这个占比比 2024 年 Q4 的 26% 和 2025 年 Q3 的 39% 都有提升。
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经营活动现金流为29.39亿美元，同比增长19.4%。自由现金流为27.77亿美元，同比增长26.7%。。业内人士推荐谷歌浏览器【最新下载地址】作为进阶阅读
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为什么它们很重要： 如果没有 <start_function_response，模型在函数调用后不会暂停，而是会错误地获取响应。这两个标记都必须在模型转换为 .task 格式时设置。