This hub collects my writing on how AI agents discover each other, delegate work, use standards like A2A and MCP, and turn protocol ideas into real operating systems.
Read Building with A2A for the architecture model, then compare it with MCP and x402.
Delegation, agent discovery, protocol layering, memory, payments, and practical orchestration patterns.
Cloudflare and devtools for infrastructure, or finance archive for older long-form writing.
How I built a discovery platform for A2A and x402 protocol implementations — featuring automated security reviews, an x402-powered Discovery API, and the infrastructure that lets agents find other agents.
How I run a content pipeline with specialized AI agents — researcher, writer, editor, publisher — and the orchestration patterns that actually work at scale.
Three patterns from the Recursive Language Models paper that changed how I orchestrate multi-agent systems — and you don't need a research lab to use them.
Forget monetization — x402's most practical use case might be as a spam filter. How a $0.99 payment requirement eliminates low-quality submissions without blocking legitimate users.
A practical architecture guide to Google's Agent-to-Agent (A2A) protocol, including agent cards, task flow, lifecycle management, and where A2A fits next to MCP and x402.
Build AI agent skills once, use everywhere. Learn the SKILL.md standard that makes capabilities portable across platforms with real migration examples.
Complete guide to migrate OpenClaw to a new server or platform. Transfer memory, cron jobs, secrets, and skills to IronClaw, Nanobot, Pi, or Goose without losing data.
How to set up x402 payment protocol on a static site — letting AI agents pay USDC per API call while humans browse for free. A practical walkthrough using Cloudflare Workers and Base.
Practical lessons from setting up and operating a multi-agent AI system — architecture decisions, cost management, and the surprising compound effects of small automations.