西子洁能于2026 年2 月26日发布公告称，公司接受了浙商证券、兆顺基金、浙商资管等7家机构的调研。西子洁能专注于节能锅炉、新能源装备、储能及清洁能源综合利用解决方案的研发、生产与服务，核心产品广泛应用于火力发电、新能源储能、工业节能、环保能源等领域。公司深耕清洁能源装备行业多年，在高效节能技术、光热储能、氢能装备等方向形成技术布局，深度契合能源绿色低碳转型趋势。此次调研体现出资本市场对其在新能源装备升级、储能业务拓展背景下的经营质量与成长潜力的关注。
Александра Лисица (Редактор отдела «Забота о себе»)
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「我上任前十個月結束了八場戰爭」
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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.
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