the-anatomy-of-an-ai-agent.md
The anatomy of an AI agent
AI agents are starting to look like operating systems. The LLM is the CPU, the agent is the OS, and skills and MCPs are the applications — here's why that analogy holds up.
Networking.__init__
! networking, automation, and the occasional LLM experiment
router#Welcome to my blog
Translating "Welcome"...domain server (255.255.255.255)
! Latest Articles
ai-makes-the-easy-part-easier-network-engineering.md
AI Makes the Easy Part Easier and the Hard Part Harder — A Network Engineering Perspective
AI can generate Cisco configs and Terraform plans with impressive fluency, but the hard part of network engineering was never the syntax. It's interop, requirements, and the mental models we build to design and troubleshoot.
building-a-system-for-ai-assisted-engineering.md
Building a System for AI-Assisted Engineering
Moving beyond vibe coding to structured AI-assisted development — using a .ai/ directory and project constitution to give AI assistants the context they need to be genuinely useful.
! Recent Syslog
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Worth watching: The 5 Levels of AI Coding. A solid framework for thinking about where you actually sit on the AI-assisted development spectrum — from basic autocomplete all the way to fully autonomous agents. Honest about what each level demands from the engineer and where the real productivity gains (and risks) live.
Most of us are somewhere in the middle and kidding ourselves about it. Good gut check.
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Watched the Cisco Live 2026 Amsterdam opening keynote. Cisco is going full AI mode — no surprise there, but interesting to see how they’re positioning it across the portfolio. Will be following the rest of Cisco Live remotely to catch the technical sessions and see what’s actually substance vs. slide deck hype.
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Testing Claude Code’s new Agent Teams feature — spinning up multiple specialized agents that work in parallel. The tradeoff is clear: order of magnitude speed, order of magnitude risk of technical debt. But having multiple domain experts collaborating (instead of one generalist context-switching) does help catch things.
Already used it to build out this Astro blog. Next experiment: ChessKids — an AI-powered chess tutorial for my 6-year-old daughter. Teaching chess to kids feels like a good test case for agentic workflows: visual, structured rules, incremental difficulty.