The models know everything about the world and nothing about how I work. This is the system I built to close that gap.
Out of the box, every AI session starts from zero. The model is impressive; the coworker is blank. It doesn't know my projects, my standards, or the fact that I already solved this exact problem twice.
The Builder's Net is what I run instead. Work I repeat becomes a reusable skill. Skills that prove themselves get promoted into agents. Agents that stop earning their keep are retired, and their useful knowledge stays behind. The network gets faster over time not because the model changed, but because it accumulated how I work. It's the same simple, composable approach Anthropic lays out in Building Effective Agents.
The first build is the slowest one. Every build after it starts with what the previous ones learned.
Nothing promotes, deploys, or goes live without my explicit sign-off.
Agents unused for 30 days get mined for anything worth keeping, then retired.
The full structure took about two days to build, not because the architecture is complex, but because each agent needs time to learn how I actually work.
Pick a scenario. Watch how the network routes it.
Known bug. Existing template match. Human not required.
Every completed task leaves an immutable record: what tools were used, which patterns applied, what the outcome was. You don't configure this, it just happens.
What would your current process look like if every decision left a trace?
The 100th build is faster than the 1st, not because the model improved, but because the network kept what the 99 before it learned. It's the same orchestrator-worker pattern behind Anthropic's multi-agent research system, where a lead agent delegating to parallel subagents outperformed a single agent by 90%.
Hover to trace connections · Click for details
Autonomous entity with its own context window and tool access. Makes decisions, delegates work, escalates when uncertain.
User-invoked action that produces structured output. You run it when you need it — it doesn’t run itself.
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.
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.
Starter files and success criteria for every agent in the network. Download, customize, then load with /load.
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.