Warming up your knowledge graph

Your knowledge graph starts cold. That’s not a flaw — it’s the honest starting point of any context system that refuses to guess. Warming it up is a sequence of phases, most automated, a few deliberately human.

The phases

Phase	What happens	Automated, or you?
1 · Harvest	Agents pull schemas, lineage, query history, and dbt metadata from your connected systems into the graph, every fact stamped with its source.	Automated, once systems are connected and verified
2 · Reconcile	The same table seen through Snowflake, dbt, and your catalog is merged into one entity; conflicts are surfaced, not silently resolved.	Automated, with conflicts queued for you
3 · Enrich	Agents propose descriptions, ownership guesses, and quality annotations — as proposals, with confidence attached.	Automated proposals
4 · Curate	Your team reviews proposals and adds what no system contains: business definitions, the “revenue means ARR for finance” rules, the no-touch lists.	You — this is the part only your team knows
5 · Promote	Facts your team stands behind are promoted to authoritative — each promotion approved by a named human.	You, with the gate enforced by the platform

After warm-up, governed agent write-back keeps the graph improving with use — and the self-maintaining refresh loop, once enabled for your deployment, re-harvests on a standing schedule (see the note on the Context Wizard page).

A practical warm-up plan

1. Pick one schema that matters. analytics beats everything — a scoped harvest completes fast and is checkable by a human who knows the data.
2. Run the harvest (Scale: from the Context Wizard surface; Start: agents harvest as they work). Spot-check: do the entity counts look like your estate? If something looks missing, say so — don’t promote around a gap.
3. Clear the reconcile queue. Every merge conflict you resolve teaches the graph your naming reality.
4. Curate the twenty facts that matter most. The top tables’ real definitions and owners deliver more agent-answer quality than a thousand auto-descriptions.
5. Promote deliberately. Authoritative facts are the ones agents will state without hedging — promote what you’d defend in a meeting.
6. Expand scope one schema at a time, repeating 2–5.

How you know it’s working

Ask an agent a question whose answer lives in the graph — “Who owns fct_orders and what does net_revenue actually mean here?” A warm graph answers with provenance; a cold one says it doesn’t know yet. Both are correct behavior — the difference is warmth, and the fix is the next phase of this page, not a workaround.