jing09928

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 consistent policy and audit behavior.

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？

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?

The Vercel-for-MCP framing is useful; the hard part seems like permissions and audit trails once tools cross org boundaries. Are policies enforced per server/app, or at each tool call?

The interesting bit is making cloud cost a first-class constraint for the agent loop, not just a post-hoc report. I'd be curious how you handle confidence/uncertainty in estimates, since a wrong cheap-looking recommendation can be worse than no estimate in infra PRs.