Hand-labelling the 09-22 10:00-12:00 ET replay window (server-26#170,
answer key replay_groundtruth_0922.json) found ~25 real incidents, of which
only ~5 had an audible clear — most jobs clear by MDT, so the quiet timer is
the close for most incidents and a flat 90 minutes left a lockout or a plate
check "active" on the portal an hour after it ended.
- summarizer: timer close after 30 min quiet for routine/minor, 60 moderate,
90 major/unknown. A timer close is provisional: reopenable=True.
- correlator: reopenable incidents inside incident_reopen_window_minutes
(90, since last substantive call) stay candidates; linking a call to one
reopens it (status active, reopened_count++). The sweep expires the flag
so the reopenable pool stays bounded. Real clears (units_cleared,
llm_closure) are never reopenable.
- cap: incident_max_calls counts substantive calls only
(substantive_call_count). The bridge MVA hit 40 in 32 min with ~40% thin
replies, split in half, and the second half took another job's title.
c2-core: 467 pass.
Co-Authored-By: Claude Opus 5.5 <noreply@anthropic.com>
First replay (290 calls, 09-22 10:00-12:00 ET) produced 0 incidents and
no errors: every gpt-4o-mini extraction failed and _sync_extract
swallowed it as "no scenes". Same shape as #169 — and the live extraction
tier in /health/ai had no reporter at all, so this has been invisible in
production too.
- intelligence: API failures propagate out of _sync_extract; extract_scenes
reports them to ai_health ("extraction" tier, billing/dead-model
classified) and still returns [] so the pipeline degrades as before.
- ai_health: inside a replay sandbox, failures go to the run's own sink
instead of being dropped.
- replay: aborts after 5 permanent failures on a tier, naming the cause;
run metrics carry ai_failures; UI shows them.
- replay estimate: audio minutes from started_at/ended_at (no duration
field exists on call docs).
- ReplayTab exposes the loaded run on window.__drbReplay for in-page
analysis.
c2-core: 458 pass.
Co-Authored-By: Claude Opus 5.5 <noreply@anthropic.com>
Correlation has only ever been measured through live AI windows: days of
wall time per change, and the 09-20→22 window was invalidated outright by
unfunded AI accounts (#169). Recordings are kept regardless of AI, so the
traffic to measure against already exists.
- internal/replay.py: runs a time range of real calls through the live
pipeline code in original order, clock pinned per call, into
replay_runs/{run_id}/calls|incidents. Modes: audio (re-transcribe),
transcripts (re-extract), reuse (correlation only from a prior run's
scenes). Simulates the idle-resolve and orphan-recorrelation sweeps on
virtual time. No alerts, summaries, vocab, AI-health alerts or pending
terms. One run at a time, <=5000 calls, <=7 days.
- firestore.py: ContextVar sandbox redirect for calls/incidents.
- clock.py: ContextVar-pinnable now(), used on the correlation path.
- feature_flags.py: ContextVar flag override so replay runs with live AI off.
- upload.py: scene loop extracted to _extract_and_correlate, shared by the
live pipeline and replay so replay measures the code that runs live.
- resolved_via on every incident resolve, so a real clear can be told
from the idle timeout — live and in replay.
- routers/replay.py + /admin Replay tab: estimate, start, compare runs,
drill into incidents with audio.
Reviewed by drb-correlation-review; its leak and fidelity findings are
fixed and covered by tests. c2-core: 456 pass. Frontend typecheck not run
(no Node on the authoring box).
Co-Authored-By: Claude Opus 5.5 <noreply@anthropic.com>
Logan CusanoandClaude Opus 5.5