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.
构建靶向精准的对“人”监督体系。实现精准监督的关键在于精准锁定监督对象。过去，个别领域一度存在监督对象界定不够清晰的问题，监督执纪容易陷入大海捞针困境。建设数字纪检监察体系，必须在精准上着力。一方面，为“一把手”与年轻干部等建立专项监督模型，强化常态化风险预警，推动监督关口前移；另一方面，整合资产、税务等数据，构建廉洁风险评估模型，推动监督关口前移。通过画像标线、动态核查、精准研判，推动纪检监察工作实现从被动接访到主动预警的转变。
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