AI knows a lot about things without you telling it. What it doesn't know is how you work. This is how you teach it.
Scroll down to watch the network grow. Each phase adds new capabilities. Click any node to learn what it does and download a starter file.
The full agent structure took about two days to build. Not because the architecture is complex — but because each agent needs time to learn how you actually work.
Autonomous entity with its own context window and tool access. Makes decisions, delegates work, escalates when uncertain.
Domain knowledge that loads into an agent’s context on demand. No autonomy of its own — it’s expertise the agent borrows when the task requires it.
Deterministic trigger that fires on specific events. No AI judgment — pure pattern matching. Runs every time, no exceptions.
Recurring background process on a schedule. Scans, monitors, proposes — but never acts without human approval.
User-invoked action that produces structured output. You run it when you need it — it doesn’t run itself.
Every build leaves a trace. The Knowledge Curator scans these weekly.
Same tools + same domain + same outcome = proposed skill.
Needs its own tools or decisions? Proposed as an Agent. Human approves.
Unused 30 days? Flagged. Useful knowledge becomes a skill. Otherwise gone.
The 100th build is fundamentally faster than the 1st — not because the model improved, but because the network accumulated institutional knowledge from the 99 before it.
Disclaimer: This case study shares ideas based on personal experience. It is not professional advice and does not guarantee results. Do your own research, test in your own environment. Downloadable files are starter templates, not production-ready configurations.