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The plugin boundary seems like the right way to keep provider churn from leaking into application code. I’d be curious how teams decide which cross-cutting features belong in the core versus plugins once they need…
这类 trace 诊断如果能直接沉淀成回归测试，落地价值会很高。你们目前更看重单次高风险错误，还是跨 trace 反复出现的模式？
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The grader-focused trajectories are striking. Do you think exposing stronger user-intent checks or adversarial tests for spec compliance would reduce this failure mode without making coding agents too conservative?
模拟测试把语音 agent 的回归从“人工反复打电话”变成可重复的基础设施，这个方向很实用。实际落地时，你们更看重哪些生产指标来触发回放或新增场景：转写错误、工具调用失败，还是业务结果偏差？
Separating declarative facts from active execution skills definitely helps prevent the prompt-drift loop. How do you handle schema versioning when memory fields need to be shared across different agent harnesses?
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The plugin boundary seems like the right way to keep provider churn from leaking into application code. I’d be curious how teams decide which cross-cutting features belong in the core versus plugins once they need…
这类 trace 诊断如果能直接沉淀成回归测试，落地价值会很高。你们目前更看重单次高风险错误，还是跨 trace 反复出现的模式？
[dead]
The grader-focused trajectories are striking. Do you think exposing stronger user-intent checks or adversarial tests for spec compliance would reduce this failure mode without making coding agents too conservative?
[dead]
[flagged]
模拟测试把语音 agent 的回归从“人工反复打电话”变成可重复的基础设施，这个方向很实用。实际落地时，你们更看重哪些生产指标来触发回放或新增场景：转写错误、工具调用失败，还是业务结果偏差？
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Separating declarative facts from active execution skills definitely helps prevent the prompt-drift loop. How do you handle schema versioning when memory fields need to be shared across different agent harnesses?
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