A senior Anthropic engineer just dropped 11-page PDF on "Loop Engineering" for agentic systems.
The shift: you stop prompting the agent. You build the system that prompts it instead.
Schedule → Discover → Build → Verify → Repeat
Every loop runs one turn, five moves:
• Discovery: it finds its own work - failing CI, open issues, recent commits - instead of being handed a list.
• Handoff: each task gets an isolated git worktree so parallel agents don't collide.
• Verification: a second agent, told to assume the code is broken, reviews the first. The "thing that can say no."
• Persistence: results get written to disk, never left in a context window that gets flushed.
• Scheduling: an automation wakes it on a timer. That's what makes it a loop.
The key insight: an agent grading its own work always praises it.
This 11-page PDF changed how I'm building agentic systems today.
Read it now, then explore the article below.
If you have:
Hermes Agent
Claude Code & Codex Handoffs
Obsidian + QMD Memory System
Run Agentic Loops
Fleet Tailscale Mesh
Cron Jobs + Kanban Board
Agentic Workflows
Congrats you are the top 1% of the AI god stack
Someone built an open-source engine that runs 500 AI agents with different personalities to debate any news story in real time.
They post, argue, and change each other's minds. Hour by hour. On your laptop.
🚨 You need to see this.
@addyosmani from Google just dropped his new Agent Skills and it's incredible.
It brings 19 engineering skills + 7 commands to AI coding agents, all inspired by Google best practices 🤯
AI coding agents are powerful, but left alone, they take shortcuts.
They skip specs, tests, and security reviews, optimizing for "done" over "correct." Addy built this to fix that.
Each skill encodes the workflows and quality gates that senior engineers actually use: spec before code, test before merge, measure before optimize.
The full lifecycle is covered:
→ Define - refine ideas, write specs before a single line of code
→ Plan - decompose into small, verifiable tasks
→ Build - incremental implementation, context engineering, clean API design
→ Verify - TDD, browser testing with DevTools, systematic debugging
→ Review - code quality, security hardening, performance optimization
→ Ship - git workflow, CI/CD, ADRs, pre-launch checklists
Features 7 slash commands: (/spec, /plan, /build, /test, /review, /code-simplify, /ship) that map to this lifecycle.
It works with:
✦ Claude Code
✦ Cursor
✦ Antigravity
✦ ... and any agent accepting Markdown. Baking in Google-tier engineering culture (Shift Left, Chesterton's Fence, Hyrum's Law) directly into your agent's step-by-step workflow!
`npx skills add addyosmani/agent-skills`
Free and open-source.
Repo link in 🧵↓
79% of enterprises have AI agents. 11% made it to production. That's not a deployment gap, it's 68 percentage points of demos that couldn't survive contact with a real database error at 2am.
Final setup for AroundUs's Agentic tech team (Admin, Dev, UI/UX) :
- Green : ready to work
- Blue : working
- Red : Needs input
LED Device : Luxafor Flag (~30€) or RP2040-Zero (~5€)