Building an AI agent is easy. Keeping one alive for six months is not.
You can wire up an LLM and a tool in an afternoon. What's hard — what actually separates a demo from a production system — is an agent that runs for hours, touches dozens of tools, survives a restart, refuses the dangerous command, and doesn't quietly forget everything it learned yesterday.
None of that comes from the model. It comes from what's around the model.
A production agent isn't User → Prompt → LLM → Tool → Answer. It's a harness — context assembly, tiered memory, permissioning, subagents, lifecycle hooks — with the LLM sitting inside it as the reasoning component, not the whole system. Below are 12 patterns for building that harness, with notes on which ones I've actually shipped.
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