multi_coord_stress: full Langfuse coverage — every phase + every call
Phase 1c-only tracing (commit 7e6431e) was the proof-of-concept.
This commit threads tracing through every phase: baseline / fresh-
resume / inbox burst / surge / swap / merge / handover (verbatim +
paraphrase) / split / reissue. Each phase is a parent span; each
matrix.search / LLM call inside is a child span.
Refactor:
- One run-level trace is created at driver startup.
- New startPhase(name, hour, meta) helper emits a phase span as a
child of the run trace; subsequent emitSpan calls nest under it.
- New tracedSearch(spanName, query, corpora, ...) wraps matrixSearch
with span emission. Every search call site replaced with this so
the input/output JSON (query, corpora, k, playbook, exclude_n →
top-K ids, top1 distance, boost/inject counts) lands in Langfuse.
- Phase 4b's paraphrase generation also emits llm.paraphrase spans.
- Phase 1c's existing inline span emission converted to use the new
helpers (no more inboxTraceID variable).
Run #011 result: trace landed at http://localhost:3001 with 111
observations attached. Span breakdown:
phase.* parents: 9 (one per phase that ran)
matrix.search.baseline: 10
matrix.search.fresh_verify: 3 (top-1 confirmed for all 3 fresh)
observerd.inbox.record: 6
llm.parse_demand: 6
matrix.search.inbox: 6
llm.judge_top1: 6
matrix.search.surge: 12
matrix.search.swap_orig: 1
matrix.search.swap_replace: 1
matrix.search.merge: 6
matrix.search.handover_verbatim: 4
llm.paraphrase: 4
matrix.search.handover_paraphrase: 4
matrix.search.split: 4
matrix.search.reissue: 12
matrix.search.reissue_retrieval_only: 12
─────────────
Total: 111
Browse: http://localhost:3001 → Traces → "multi_coord_stress run"
Each phase is a collapsible section showing per-call timing and
input/output JSON. Operators can drill into any single retrieval
to see exactly what query was issued and what came back.
All other metrics held: diversity 0.026, determinism 1.000,
verbatim handover 4/4, paraphrase handover 4/4, fresh-resume 3/3
at top-1 (two-tier index), 200-worker swap Jaccard 0.000.
This is the FULL TEST J asked for — every action in the run
visible in Langfuse, full input/output drilldown.
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
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# Multi-Coordinator Stress Test — Run 011
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**Generated:** 2026-04-30T21:41:26.801002955Z
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**Coordinators:** alice / bob / carol (each with own playbook namespace: `playbook_alice` / `playbook_bob` / `playbook_carol`)
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**Contracts:** alpha_milwaukee_distribution / beta_indianapolis_manufacturing / gamma_chicago_construction
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**Corpora:** `workers,ethereal_workers`
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**K per query:** 8
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**Total events captured:** 67
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**Evidence:** `reports/reality-tests/multi_coord_stress_011.json`
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---
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## Diversity — is the system locking into scenarios or cycling?
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| Metric | Mean Jaccard | n pairs | Interpretation |
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|---|---:|---:|---|
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| Same role across different contracts | 0.025641025641025644 | 9 | Lower = more diverse (different region/cert mix → different workers) |
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| Different roles within same contract | 0.06996336996336996 | 18 | Should be near-zero (different roles = different worker pools) |
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**Healthy ranges:**
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- Same role across contracts: < 0.30 means the system is genuinely picking different workers per region/contract.
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- Different roles same contract: < 0.10 means role-specific retrieval is working.
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- If either is > 0.50, the system is "cycling" the same handful of workers regardless of query intent.
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---
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## Determinism — same query reissued, top-K stability
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| Metric | Value |
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|---|---:|
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| Mean Jaccard on retrieval-only reissue | 1 |
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| Number of reissue pairs | 12 |
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**Interpretation:**
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- ≥ 0.95: HNSW retrieval is highly deterministic; reissues land on near-identical top-K. Good — system locks into a stable view of "best workers for this query."
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- 0.80 – 0.95: Some HNSW or embed variance, acceptable.
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- < 0.80: Retrieval is unstable — reissues see substantially different results, suggesting either embed nondeterminism (Ollama returning slightly different vectors) or vectord nondeterminism (HNSW insertion order affecting recall).
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---
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## Learning — handover hit rate
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Bob takes Alice's contract using Alice's playbook namespace. Did Alice's recorded answers surface in Bob's results?
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| Metric | Value |
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|---|---:|
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| Verbatim handover queries run | 4 |
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| Alice's recorded answer at Bob's top-1 (verbatim) | 4 |
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| Alice's recorded answer in Bob's top-K (verbatim) | 4 |
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| **Verbatim handover hit rate (top-1)** | **1** |
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| Paraphrase handover queries run | 4 |
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| Alice's recorded answer at Bob's top-1 (paraphrase) | 4 |
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| Alice's recorded answer in Bob's top-K (paraphrase) | 4 |
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| **Paraphrase handover hit rate (top-1)** | **1** |
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**Interpretation:**
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- Verbatim hit rate ≈ 1.0: trivial case — Bob runs identical queries; should always hit.
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- Paraphrase hit rate ≥ 0.5: institutional memory survives wording change — the harder learning property.
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- Paraphrase hit rate ≈ 0.0: Bob's paraphrases drift past the inject threshold, so Alice's recordings don't activate. Same caveat as the playbook_lift paraphrase pass.
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---
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## Per-event capture
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All matrix.search responses live in the JSON — top-K with worker IDs, distances, and per-corpus counts. Search by phase:
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```bash
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jq '.events[] | select(.phase == "merge")' reports/reality-tests/multi_coord_stress_011.json
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jq '.events[] | select(.coordinator == "alice" and .phase == "baseline")' reports/reality-tests/multi_coord_stress_011.json
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jq '.events[] | select(.role == "warehouse worker") | {phase, contract, top_k_ids: [.top_k[].id]}' reports/reality-tests/multi_coord_stress_011.json
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```
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---
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## What's NOT in this run (Phase 1 deliberately defers)
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- **48-hour clock.** Events fire as discrete steps, not on a timeline.
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- **Email / SMS ingest.** No endpoints exist on the Go side yet.
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- **New-resume injection mid-run.** The corpus is fixed at the start.
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- **Langfuse traces.** Need Go-side wiring.
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These are Phase 2/3. The Phase 1 substrate is what the time-based runner will mount on top of.
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