Every AI session used to start from zero. Whatever an assistant learned about my work, my network or my priorities evaporated when the chat closed, so each session paid the same tax of re-explaining everything. Dave, my failed agent experiment, proved the point expensively: without governed, persistent context, even a well-built agent is useless.
I built the context layer first and gave it a written operating contract. Facts live in a fifteen-table relational Airtable base, with every capture and interaction landing in a ledger. Tasks live in ClickUp, and only in ClickUp. Meaning, the career narrative, the project stories, the judgement, lives in an Obsidian wiki that LLMs maintain under written rules: what they may do freely, what needs my sign-off, and what they must never touch. Scheduled agents and n8n automations keep it fed: a daily note drains itself into the ledger every evening, my LinkedIn network syncs into the database and enriches itself, and a monthly sweep tidies what the capturing left rough.
Any AI session, chat or code, now picks up my full working context in seconds and leaves the system better than it found it. Capture is instant and goes through triage rather than guesswork, records carry their own audit trail, and the system has survived three major rebuilds because the contract, not the tooling, is the constant. It is the build I am proudest of and the one I run my whole life on.
Write the contract before you grant the autonomy. Every failure mode I have hit came from ambiguity about what the system should do, never from the tools. Verify by output evidence rather than trusting a green tick, keep facts, tasks and meaning in separate homes, and let the records earn their keep: capture freely, enrich monthly, kill what earns nothing.