Three real-shape demand queries against the long-running 11-daemon
stack with 500 workers ingested from workers_500k.parquet (real
production data). Substrate is producing useful answers:
Q1 (Forklift @ Aurora IL): 5/5 role match, top 3 in IL, dist 0.44-0.46
Q2 (CNC @ Detroit MI): top-1 in Detroit MI exactly, role pulls
Machine Operator (semantic neighbor)
Q3 (Warehouse @ Indianapolis IN): top-1 in Indianapolis IN, 5/5 role
match, dist 0.42-0.54
This is the FIRST end-to-end coordinator-shape query against the
persistent Go stack — every prior reality test (real_001..real_005)
ran through harness-transient stacks that died on exit. This one
ran against daemons that have been up for minutes and stayed up
through retrieval.
Geo is load-bearing: top-1 city/state matched in 3/3 queries.
Embedder treats geography as a primary feature.
Q2's CNC→Machine Operator gap exposes the playbook learning loop's
purpose: judge would rate this ~3/5; the first time a coordinator
approves a Machine Operator for a CNC Operator query, that
recording starts shifting substrate behavior. That's the loop
we've been building toward — the persistent stack is now the
substrate that loop will run on.
Evidence: reports/cutover/persistent_stack_first_query.md (full
top-K tables + read on each query).
What this does NOT prove:
- Production-volume load (3 queries, 500 workers)
- Concurrent latency
- Full 5-loop substrate (this exercised retrieval only; no
playbook recordings exist on the persistent stack yet)
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
