Browsing Tag
agents
66 posts
Monitoring OpenAI Agents in Production: Beyond the Obvious Metrics
You know that feeling when your OpenAI agent starts behaving weirdly at 3 AM and you have no…
AAEF v0.6.0: Practical Adoption Readiness Planning Release
I’ve published AAEF v0.6.0. AAEF — Agentic Authority & Evidence Framework — is an action assurance control profile…
The Failure Your AI Agent Can Never See Published
On April 11, 2026, I ran an AI agent in production and it hit a rate limit. My…
One Open Source Project a Day (No.50): The TypeScript Wizard Pushed His .claude Directory to GitHub and Hit #1 Worldwide Overnight
Introduction “My agent skills that I use every day to do real engineering — not vibe coding.” —…
My Harness Is Not a Cage. It’s an Org Chart.
Your AI agent did not fail because the model was weak. It failed because it made a decision…
What 221 AI Agents in One Chat Taught Us About Multi-Agent Coordination
When Stanford published the Smallville paper in 2023, twenty-five generative agents living in a simulated town felt like…
Taming MCPs, Skills, and Agent Chaos with ToolHive
I use a lot of AI coding tools: OpenCode, Antigravity, Claude Code, Codex, Gemini CLI, Pi, Zed, GitHub…
AI治理最重要的能力：缺乏证据支持时懂得暂停
1）观点先行（P0） 一句话观点： 在 AI 协作里，最有价值的治理能力不是“更快修完”，而是“证据不够时敢停下，并把缺什么证据说清楚”。 2）治理背景（P1） 复杂系统里的真实问题，不是没人干活，而是大家都在干活，却很难判断到底有没有真的完成。 AI 参与后，这个问题会更明显： AI 很容易给出“看起来已经完成”的答案。 多个智能体并行提交回执，信息会很快变成噪音。 模块测试通过，常常被误读成系统已经恢复。 本地治理体系之所以更快，不是因为流程更短，而是因为它把“没完成”这件事制度化了： 可以停在中间状态。 可以明确写出阻断原因。 可以等证据补齐后再推进状态。 3）信号提取（P0）…
Agent Memory: A Free Short Course on Building Memory-Aware Agents
Oracle and DeepLearning.AI have launched Agent Memory: Building Memory-Aware Agents, a free short course on DeepLearning.AI that teaches…
Chapter 1 Deep-Dive: What Amplification Actually Looks Like
Companion document to “Software Development in the Agentic Era” By Mike, in collaboration with Claude (Anthropic) The main…
