As agents move from demos to unsupervised work, the bottleneck shifts from can it do the task to can you trust it to. I build the reliability, verification, and provenance infrastructure that makes autonomous agents safe to ship. 当 agent 从 demo 走向无人值守的真实工作,瓶颈就从「它能不能做」变成「你敢不敢信它做」。我构建让自主 agent 能放心上线的可靠性、验证与溯源基础设施。
No live demo here — these are recorded runs, replayed. Logs are claims; replays are proofs.这里没有 live demo——只有录制的运行与回放。Logs are claims; replays are proofs.
Software taught us that capability comes first, then testing, monitoring, and CI become mandatory infrastructure. AI agents are walking the same path. My work sits in that emerging trust layer — the picks and shovels of the agent economy. 软件的规律是:能力先到,然后测试、监控、CI 成为强制基建。AI agent 正在重走这条路。我的工作就处在这层正在形成的信任基建里——agent 经济的铲子。
More: provenant — glass-box bill auditing, HMAC-signed proof receipts · simp-skill ⭐240+ · RAG-learning更多:provenant——玻璃盒账单审计,HMAC 签名证明收据 · simp-skill ⭐240+ · RAG-learning
I'm an agent engineer focused on what happens after the demo works: making AI agents reliable, verifiable, and trustworthy enough to run without a human watching. I like the unglamorous infrastructure — replay harnesses, proof receipts, eval gates — that turns a clever prototype into something you can actually depend on. 我是一名 agent 工程师,专注于「demo 跑通之后」的事:让 AI agent 足够可靠、可验证、可信,能在没人盯着的情况下运行。我喜欢那些不性感的基础设施——重放框架、证明收据、评测门禁——正是它们把一个聪明的原型,变成真正能依赖的东西。