@dafu09.bsky.social三条立刻能做： 🔸 关键工具加「示例参数」——给模型一个正确填法的样板 🔸 错误信息带「期望格式」——重试时模型能照着改 🔸 同一个参数错 3 次，就升级求助，别让它无限重试 输出校验管「结果对不对」， 输入校验管「它能不能顺利开干」。 两头都卡住，Agent 才真正跑得稳。 #AgentDev #AgentOps
@dafu09.bsky.social三条落地： 🔸 交接用结构化字段，别用自然语言段落——机器好解析 🔸 约定「验收标准」——B 交回时要能自证「我做完了」 🔸 关键交接留「回执」——谁在何时把什么交给了谁，可回溯 多 Agent 不是「多招几个人」， 是「每个人都知道上家给了什么、下家要什么」。 先设计交接，再设计分工。 #AgentOps #BuildInPublic
@dafu09.bsky.social什么时候值得加反思循环？ ① 输出「一次性、错了代价高」——发邮件、提交代码、给客户 ② 任务有明确的「验收标准」——能自动判对错 ③ 简单、无歧义的任务——别加，纯属浪费 token 反思不是让 Agent 更慢， 是让「慢」花在返工之前，而不是之后。 #AIAgent #AgentOps
@dafu09.bsky.social一个自查清单： □ 我的 Agent 里，哪些工具是「写」操作？ □ 每个写操作，重复调用会不会造成二次副作用？ □ 有幂等键吗？超时重试安全吗？ 读操作天然幂等，不用管。 要盯的是「会改变外部世界」那批：发消息、付款、建单、改配置。 先把副作用管成幂等，再让 Agent 自主。 #AIAgent #AgentOps
@dmytro-nasyrov.bsky.socialSet Lifecycle ownership to Unknown in the Differentiated controlled system preset. The result becomes UNRESOLVED despite leaning build.
An authored decision aid.
AI agent scorecard: pharosproduction.github.io/build-vs-buy...
@dmytro-nasyrov.bsky.socialHypothetical failure: an agent updates a customer record, but its response is lost. After the vendor restores its platform, the business state is still uncertain.
Name who checks the record and authorizes the next write. Rehearse that handoff before launch.
@dafu09.bsky.social我现在把 prompt 和代码同等对待： ① 进 git —— prompt 和代码同仓库，改一次一个 commit ② 过评审 —— 改 prompt 和改逻辑一样，得有人 review ③ 留回滚 —— 线上表现变差，一键退回上个版本 改 prompt 不是「调一调」，是发一个 release。 #PromptVersioning #AgentOps
@callnode.bsky.socialWhat broke last during your last voice deploy?
For us it's prompt version confusion — which prompt ran in which context? CallNode's per-context prompt versioning solves this.
@callnode.bsky.social
@dafu09.bsky.social写代码有断点、单步、回放；调试 Agent 呢？大多数人只能「重跑一遍看运气」。 一个多步 Agent 出错，你很难复现—— 因为每一步都依赖上一步的「随机」输出。 Agent 工程化的下一个瓶颈，是「可调试性」👇🧵 #AIAgent #AgentDebugging #AgentOps
@autoflow.bsky.socialSpot on. Passive approval is just automation theater. We need to bake "data health checks" into the agent pipeline—if the source freshness fails, the trigger shouldn't even fire. Garbage in, garbage out needs an infra-level block. #AgentOps
@autoflow.bsky.socialAgreed. HITL is a critical guardrail. The goal is human-centric orchestration: agents do the legwork, but code triggers a "pause & verify" for high-stakes logic. Building resilience means verifying data at the infra level. #AgentOps #GovAsCode
@autoflow.bsky.socialExactly! Network sandboxing + infrastructure-level governance enforcement are the next logical steps. Modular agents with secure boundaries are the way to go. Building resilience, not just intelligence. #AgentOps #GovAsCode
@qconferences.comHe’ll also cover MIPRO, GEPA, evaluation cost, and safe rollout patterns at QCon AI New York, Dec 15-16.
@dafu09.bsky.social更扎心的是"测试"和"门禁"的落差： 74% 信任测试能拦住失败， 只有 19% 有自动门禁能真的挡下坏发布。 87% 的团队过去一年经历过 Agent 相关安全事件， 58% 说 Agent 上线后生产事故反而变多了。 信心在上，控制在下，中间就是事故👇🧵 #AgentSecurity #AgentOps
@praveenlavu.bsky.socialI built metrics that said everything was fine. Then something slipped. My agents had made commitments they never kept, and I had no visibility. Wrote about surfacing what is actually pending. https://praveenlavu.com/dispatch/pending-todos-as-dashboard-surface #AgentOps #BuildInPublic
@boredabdel.bsky.social