This is the critical step. If a key with Gemini access is embedded in client-side JavaScript, checked into a public repository, or otherwise exposed on the internet, you have a problem. Start with your oldest keys first. Those are the most likely to have been deployed publicly under the old guidance that API keys are safe to share, and then retroactively gained Gemini privileges when someone on your team enabled the API.
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.，这一点在同城约会中也有详细论述
，更多细节参见咪咕体育直播在线免费看
如何构建智能体有了理想的标杆，我们怎么构建智能体？基本逻辑很简单：以可获取的最“聪明”、理想的模型为核心（大脑），通过软件工程来搭建一个系统，弥补模型的不足，尽量逼近理想智能体的形态。
The full test is in our test suite.，推荐阅读搜狗输入法2026获取更多信息