/proof: explain the dual-agent recursive architecture with citations

Previous page was numeric claims without explanations — 'sub-100ms SQL',
'500K vectors in 341ms' etc. Accurate but undefendable without math,
code paths, and ADR references. Expanded to 8 chapters:

Ch1 — Live receipts (unchanged: real gateway tests, pass/fail, timing)

Ch2 — Architecture. 13-crate diagram with per-crate responsibility
      table and file paths. gateway → catalogd/queryd/vectord/ingestd
      + aibridge → object_store. References ADRs 1-20.

Ch3 — Dual-agent recursive consensus loop (NEW)
      - Role specialization (executor=optimist, reviewer=pessimist)
      - Parallel orchestration via Promise.all
      - Recursive: sealed playbooks feed playbook_memory → next query
      - Termination math: sealed | tool-error abort | drift abort |
        turn-cap abort — every path dumps forensic log
      - File refs: tests/multi-agent/agent.ts, orchestrator.ts,
        scenario.ts, run_e2e_rated.ts

Ch4 — Playbook memory feedback loop (NEW)
      - PlaybookEntry shape with embedding
      - Full boost math: similarity * base_weight * decay * penalty
        / n_workers, capped at MAX_BOOST_PER_WORKER
      - Temporal decay (e^-age/30, 30d half-life)
      - Negative signal (0.5^failures)
      - Why k=200: narrow cosine discrimination in nomic-embed-text
      - Evidence: compounding test 0 → 0.250 cap in 3 seeds
      - persist_sql write-through
      - Pattern discovery (Path 2 meta-index)
      - File: crates/vectord/src/playbook_memory.rs

Ch5 — ADR citations for each key choice
      ADR-001, 008, 012, 015, 019, 020 + Phase 19 design note

Ch6 — Live scale data (unchanged: pulled from /proof.json)

Ch7 — Reproduction recipes: curl for health, sql, hybrid with boost,
      patterns, pm stats, and the full dual-agent scenario run

Ch8 — Honest limits (unchanged: synthetic workers_500k, 1K candidates
      misaligned to call_log, 7B model imperfection, no rate/margin)

Every architectural claim now cites either the code path
(crates/.../src/file.rs::fn_name) or the ADR (docs/DECISIONS.md).
Someone disputing the system has specific targets to attack.

Mechanism unchanged: /proof serves mcp-server/proof.html via
Bun.file. /proof.json still returns the live test data the page
consumes client-side.

mcp-server/index.ts

@@ -631,510 +631,15 @@ async function main() {
           return new Response(await r.text(), { status: r.status, headers: { "Content-Type": "application/json" } });
         }
 
-        // Proof page — styled HTML with live tests
+        // Proof — narrative HTML served from mcp-server/proof.html.
+        // Live tests consumed client-side via /proof.json.
         if (url.pathname === "/proof") {
-          const ds = await api("GET", "/catalog/datasets") as any[];
-          const indexes = await api("GET", "/vectors/indexes") as any[];
-          const vram = await api("GET", "/ai/vram");
-          const totalRows = (ds || []).reduce((s: number, d: any) => s + (d.row_count || 0), 0);
-          const totalChunks = (indexes || []).reduce((s: number, i: any) => s + i.chunk_count, 0);
-
-          const tests: any[] = [];
-          const sqls: [string, string][] = [
-            ["COUNT 500K workers", "SELECT COUNT(*) FROM workers_500k"],
-            ["COUNT 1M timesheets", "SELECT COUNT(*) FROM timesheets"],
-            ["Filter + aggregate", "SELECT role, COUNT(*) cnt FROM workers_500k WHERE state='IL' AND CAST(reliability AS DOUBLE)>0.8 GROUP BY role ORDER BY cnt DESC LIMIT 3"],
-            ["Cross-table JOIN (800K×100K)", "SELECT COUNT(*) FROM candidates c JOIN (SELECT candidate_id, COUNT(*) calls FROM call_log GROUP BY candidate_id HAVING COUNT(*)>=5) cl ON c.candidate_id=cl.candidate_id WHERE c.city='Chicago'"],
-          ];
-          for (const [name, sql] of sqls) {
-            const t0 = Date.now();
-            const r = await api("POST", "/query/sql", { sql });
-            tests.push({ name, ms: Date.now() - t0, result: r.rows?.[0], pass: !r.error });
-          }
-          const ht0 = Date.now();
-          const hybrid = await api("POST", "/vectors/hybrid", {
-            question: "reliable forklift operator", index_name: "workers_500k_v1",
-            sql_filter: "role = 'Forklift Operator' AND state = 'IL' AND CAST(reliability AS DOUBLE) > 0.8",
-            filter_dataset: "workers_500k", id_column: "worker_id", top_k: 5, generate: false,
-            use_playbook_memory: true,
+          return new Response(Bun.file(import.meta.dir + "/proof.html"), {
+            headers: { ...cors, "Content-Type": "text/html" },
           });
-          tests.push({
-            name: "Hybrid SQL+Vector Search", ms: Date.now() - ht0,
-            result: { sql_matches: hybrid.sql_matches, verified_results: hybrid.vector_reranked },
-            pass: (hybrid.vector_reranked || 0) > 0,
-            sources: hybrid.sources?.slice(0, 5),
-          });
-
-          // Run LIVE CRM vs AI comparisons — these actually execute on page load
-          const demos: any[] = [];
-          const demoQueries = [
-            { query: "warehouse help", desc: "A staffer types what they need in plain English" },
-            { query: "someone good with machines who is dependable", desc: "Natural language — no field names, no filters" },
-            { query: "safety trained worker for chemical plant", desc: "The CRM doesn't know 'safety trained' = OSHA + Hazmat" },
-          ];
-          for (const dq of demoQueries) {
-            // CRM attempt: exact LIKE match
-            const crmResult = await api("POST", "/query/sql", {
-              sql: `SELECT COUNT(*) cnt FROM workers_500k WHERE resume_text LIKE '%${dq.query}%'`
-            });
-            const crmCount = crmResult?.rows?.[0]?.cnt ?? 0;
-
-            // AI attempt: vector search understands meaning
-            const aiResult = await api("POST", "/vectors/hnsw/search", {
-              index_name: "workers_500k_v1",
-              query: dq.query,
-              top_k: 3,
-            });
-            const aiHits = aiResult?.results || [];
-
-            demos.push({ ...dq, crmCount, aiHits });
-          }
-
-          const g = vram?.gpu || {};
-          const ts = new Date().toLocaleString();
-          const testRows = tests.map((t: any) => {
-            const icon = t.pass ? "✓" : "✗";
-            const cls = t.pass ? "pass" : "fail";
-            const val = typeof t.result === "object" ? JSON.stringify(t.result) : t.result;
-            return `<tr class="${cls}"><td>${icon}</td><td>${t.name}</td><td>${t.ms}ms</td><td>${val}</td></tr>`;
-          }).join("");
-
-          const workerRows = (hybrid.sources || []).map((s: any) => {
-            const parts = s.chunk_text?.split("—") || ["", ""];
-            const name = parts[0]?.trim();
-            const rest = parts[1]?.trim() || "";
-            return `<tr><td>${s.doc_id}</td><td>${name}</td><td>${rest.slice(0, 120)}</td><td>${s.score?.toFixed(3)}</td><td class="pass">✓</td></tr>`;
-          }).join("");
-
-          const html = `<!DOCTYPE html><html><head><meta charset="utf-8"><meta name="viewport" content="width=device-width,initial-scale=1">
-<title>Lakehouse — Proof of Work</title>
-<style>
-*{margin:0;padding:0;box-sizing:border-box}
-body{font-family:'Inter','SF Pro',system-ui,sans-serif;background:#0a0a0f;color:#d4d4d8;line-height:1.6}
-.hero{background:linear-gradient(135deg,#0f172a 0%,#1e1b4b 50%,#0f172a 100%);padding:60px 40px;text-align:center;border-bottom:1px solid #1e293b}
-.hero h1{font-size:32px;font-weight:700;background:linear-gradient(to right,#f472b6,#818cf8,#38bdf8);-webkit-background-clip:text;-webkit-text-fill-color:transparent;margin-bottom:8px}
-.hero .sub{color:#94a3b8;font-size:14px}
-.hero .ts{color:#64748b;font-size:12px;margin-top:4px}
-.container{max-width:1100px;margin:0 auto;padding:30px 20px}
-.stats{display:grid;grid-template-columns:repeat(auto-fit,minmax(200px,1fr));gap:16px;margin-bottom:40px}
-.stat{background:#111827;border:1px solid #1e293b;border-radius:12px;padding:24px;text-align:center}
-.stat .num{font-size:36px;font-weight:800;background:linear-gradient(135deg,#34d399,#22d3ee);-webkit-background-clip:text;-webkit-text-fill-color:transparent}
-.stat .label{color:#94a3b8;font-size:12px;text-transform:uppercase;letter-spacing:1px;margin-top:4px}
-section{margin-bottom:40px}
-h2{font-size:18px;color:#e2e8f0;margin-bottom:16px;padding-bottom:8px;border-bottom:1px solid #1e293b}
-h2 span{color:#818cf8}
-table{width:100%;border-collapse:collapse;font-size:13px}
-th{text-align:left;padding:10px 14px;background:#111827;color:#94a3b8;font-weight:600;text-transform:uppercase;font-size:11px;letter-spacing:0.5px}
-td{padding:10px 14px;border-bottom:1px solid #1e293b}
-tr:hover{background:#111827}
-.pass{color:#34d399} .fail{color:#f87171}
-.badge{display:inline-block;padding:3px 10px;border-radius:20px;font-size:11px;font-weight:600}
-.badge.green{background:#052e16;color:#34d399;border:1px solid #166534}
-.badge.blue{background:#0c1a3d;color:#60a5fa;border:1px solid #1e40af}
-.badge.purple{background:#1e1047;color:#a78bfa;border:1px solid #5b21b6}
-.gpu-bar{background:#1e293b;border-radius:8px;height:24px;overflow:hidden;margin-top:8px}
-.gpu-fill{background:linear-gradient(90deg,#818cf8,#38bdf8);height:100%;border-radius:8px;transition:width 0.3s}
-.note{background:#0c1a3d;border:1px solid #1e3a5f;border-radius:8px;padding:16px;color:#93c5fd;font-size:13px;margin-top:20px}
-.note strong{color:#60a5fa}
-.footer{text-align:center;color:#475569;font-size:12px;padding:30px;border-top:1px solid #1e293b;margin-top:40px}
-@media(max-width:768px){
-  .hero{padding:30px 16px}
-  .hero h1{font-size:22px}
-  .container{padding:16px 12px}
-  .stats{grid-template-columns:repeat(2,1fr);gap:10px}
-  .stat{padding:14px}
-  .stat .num{font-size:24px}
-  section{padding:16px !important;margin-bottom:20px !important}
-  table{font-size:11px;display:block;overflow-x:auto;white-space:nowrap}
-  th,td{padding:6px 8px}
-  h2{font-size:15px}
-  .g2{grid-template-columns:1fr !important}
-  .g3{grid-template-columns:1fr !important}
-  .g4{grid-template-columns:repeat(2,1fr) !important}
-}
-</style></head><body>
-<div class="hero" style="padding:50px 40px 40px">
-  <h1 style="font-size:28px">Your Morning Just Got Easier</h1>
-  <div class="sub" style="font-size:16px;color:#cbd5e1;max-width:700px;margin:12px auto 0">
-    This isn't another CRM to learn. It's your contracts, your workers, your data —<br>
-    already matched before you sit down.
-  </div>
-</div>
-
-<div class="container">
-  <section style="background:linear-gradient(135deg,#0c1220,#0f1a2e);border:1px solid #1e3a5f;border-radius:16px;padding:35px;margin-bottom:40px">
-    <h2 style="border:none;color:#e2e8f0;font-size:20px;margin-bottom:20px">We know what your day looks like</h2>
-    <div class="g2" style="display:grid;grid-template-columns:1fr 1fr;gap:24px">
-      <div>
-        <div style="color:#f87171;font-size:13px;font-weight:600;margin-bottom:12px">RIGHT NOW — without this</div>
-        <div style="color:#94a3b8;font-size:13px;line-height:1.8">
-          ☐ Open the CRM. Search "forklift" + "Chicago" + "OSHA."<br>
-          ☐ Get 200 results. Scroll through. Half are inactive.<br>
-          ☐ Cross-reference certifications in a different tab.<br>
-          ☐ Check availability in a spreadsheet.<br>
-          ☐ Check reliability from memory or ask a coworker.<br>
-          ☐ Copy names into a message. Personalize each one.<br>
-          ☐ Repeat for the next contract. And the next.<br>
-          <span style="color:#f87171;margin-top:8px;display:block">45 minutes before you make your first call.</span>
-        </div>
-      </div>
-      <div>
-        <div style="color:#34d399;font-size:13px;font-weight:600;margin-bottom:12px">WITH THIS — same morning</div>
-        <div style="color:#94a3b8;font-size:13px;line-height:1.8">
-          ✓ Open the page. Your contracts are listed by urgency.<br>
-          ✓ Workers already matched — name, skills, certs, scores.<br>
-          ✓ Only workers who are available, certified, and reliable.<br>
-          ✓ Ranked by who's the best fit, not just who comes first.<br>
-          ✓ Emergency fills flagged at the top.<br>
-          ✓ One click away from outreach.<br>
-          <br>
-          <span style="color:#34d399;margin-top:8px;display:block">You're on the phone in 5 minutes.</span>
-        </div>
-      </div>
-    </div>
-    <div style="border-top:1px solid #1e3a5f;margin-top:24px;padding-top:16px;color:#64748b;font-size:12px">
-      This isn't about replacing what you know. It's about not making you dig for it every single time.
-      You know who the good workers are — this just puts them in front of you faster.
-    </div>
-  </section>
-
-  <section style="margin-bottom:40px">
-    <h2 style="border:none;color:#e2e8f0;font-size:18px;margin-bottom:16px">Here's what it actually did — just now, when you loaded this page:</h2>
-    <div class="g3" style="display:grid;grid-template-columns:repeat(3,1fr);gap:16px;margin-bottom:20px">
-      <div class="stat" style="text-align:left;padding:20px">
-        <div style="color:#34d399;font-size:28px;font-weight:800">${hybrid.sql_matches?.toLocaleString()}</div>
-        <div style="color:#94a3b8;font-size:12px;margin-top:4px">Forklift operators in IL with 80%+ reliability</div>
-        <div style="color:#475569;font-size:11px;margin-top:2px">Found in ${tests[tests.length-1]?.ms}ms — you'd still be typing the search</div>
-      </div>
-      <div class="stat" style="text-align:left;padding:20px">
-        <div style="color:#818cf8;font-size:28px;font-weight:800">${hybrid.vector_reranked}</div>
-        <div style="color:#94a3b8;font-size:12px;margin-top:4px">Best matches ranked by AI — not alphabetical, not random</div>
-        <div style="color:#475569;font-size:11px;margin-top:2px">The system read their skills and picked the best fit for you</div>
-      </div>
-      <div class="stat" style="text-align:left;padding:20px">
-        <div style="color:#fbbf24;font-size:28px;font-weight:800">✓</div>
-        <div style="color:#94a3b8;font-size:12px;margin-top:4px">Every name verified against the actual database</div>
-        <div style="color:#475569;font-size:11px;margin-top:2px">Not guessing, not making up people. These workers are real.</div>
-      </div>
-    </div>
-
-    <div style="background:#0d0d1a;border-radius:12px;padding:20px;border:1px solid #1e293b">
-      <div style="color:#94a3b8;font-size:12px;margin-bottom:12px">Your top matches right now — ready for outreach:</div>
-      <table><thead><tr><th>Name</th><th>Details</th><th>Fit Score</th><th>Verified</th></tr></thead>
-      <tbody>${workerRows}</tbody></table>
-    </div>
-  </section>
-
-  <section style="background:#0c1220;border:1px solid #1e3a5f;border-radius:12px;padding:24px;margin-bottom:40px">
-    <div style="color:#e2e8f0;font-size:15px;font-weight:600;margin-bottom:12px">What's different from your CRM:</div>
-    <div class="g3" style="display:grid;grid-template-columns:1fr 1fr 1fr;gap:20px">
-      <div>
-        <div style="color:#818cf8;font-size:13px;font-weight:600;margin-bottom:6px">It understands what you mean</div>
-        <div style="color:#64748b;font-size:12px">Search "warehouse help" and it finds Forklift Operators, Loaders, Shipping Clerks — because it understands those ARE warehouse jobs. Your CRM would find nothing.</div>
-      </div>
-      <div>
-        <div style="color:#34d399;font-size:13px;font-weight:600;margin-bottom:6px">It already filtered the junk</div>
-        <div style="color:#64748b;font-size:12px">Inactive workers, expired certs, low reliability — already removed. You only see people you'd actually want to call. Not 200 results where 150 are useless.</div>
-      </div>
-      <div>
-        <div style="color:#fbbf24;font-size:13px;font-weight:600;margin-bottom:6px">It runs on YOUR machine</div>
-        <div style="color:#64748b;font-size:12px">No cloud. No per-search fee. No sending your worker data to someone else's server. Everything runs right here, right now, on hardware you control.</div>
-      </div>
-    </div>
-  </section>
-
-  <div style="text-align:center;padding:20px;color:#475569;font-size:13px;margin-bottom:30px">
-    — Technical details below for the team that wants to see the numbers —
-  </div>
-
-  <div class="stats">
-    <div class="stat"><div class="num">${totalRows.toLocaleString()}</div><div class="label">Total Records</div></div>
-    <div class="stat"><div class="num">${totalChunks.toLocaleString()}</div><div class="label">AI-Indexed Chunks</div></div>
-    <div class="stat"><div class="num">${indexes?.length || 0}</div><div class="label">Search Indexes</div></div>
-    <div class="stat"><div class="num">10M</div><div class="label">Max Tested Scale</div></div>
-  </div>
-
-  <section>
-    <h2><span>01</span> What a CRM Does — keyword match on ${totalRows.toLocaleString()} rows</h2>
-    <p style="color:#94a3b8;font-size:13px;margin-bottom:12px">Standard SQL filters. Fast, but only finds EXACT matches. Every CRM does this.</p>
-    <table><thead><tr><th></th><th>Query</th><th>Speed</th><th>Result</th></tr></thead>
-    <tbody>${testRows}</tbody></table>
-    <p style="color:#64748b;font-size:11px;margin-top:8px">Limitation: search for "warehouse work" finds nothing — no worker has that exact text in their profile.</p>
-  </section>
-
-  <section style="background:linear-gradient(135deg,#0f172a,#1a0f2e);border:1px solid #7c3aed;border-radius:16px;padding:30px;margin:30px 0">
-    <h2 style="border:none;color:#a78bfa;font-size:20px;margin-bottom:8px">See the difference — live, right now</h2>
-    <p style="color:#c4b5fd;font-size:13px;margin-bottom:24px">
-      These searches just ran on ${totalRows.toLocaleString()} real worker profiles when you loaded this page.
-      Left: what your CRM finds. Right: what AI finds. Same search, same data.
-    </p>
-
-    ${demos.map((d: any, i: number) => {
-      const aiNames = d.aiHits.map((h: any) => {
-        const name = h.chunk_text?.split("—")[0]?.trim() || h.doc_id;
-        const role = h.chunk_text?.match(/— (.+?) in/)?.[1] || "";
-        const city = h.chunk_text?.match(/in (.+?)\./)?.[1] || "";
-        return { name, role, city, score: h.score };
-      });
-
-      return `
-    <div style="margin-bottom:${i < demos.length - 1 ? '24px' : '0'};padding-bottom:${i < demos.length - 1 ? '24px' : '0'};border-bottom:${i < demos.length - 1 ? '1px solid #2d1b69' : 'none'}">
-      <div style="color:#94a3b8;font-size:12px;margin-bottom:10px">${d.desc}</div>
-      <div style="background:#0a0a14;border-radius:8px;padding:14px 18px;margin-bottom:12px;font-size:18px;color:#e2e8f0;font-weight:600">
-        "${d.query}"
-      </div>
-      <div class="g2" style="display:grid;grid-template-columns:1fr 1fr;gap:16px">
-        <div style="background:#1a0a0a;border:1px solid #7f1d1d;border-radius:8px;padding:16px">
-          <div style="color:#f87171;font-size:11px;text-transform:uppercase;letter-spacing:1px;margin-bottom:8px">Your CRM (keyword match)</div>
-          <div style="color:#fca5a5;font-size:32px;font-weight:800">${d.crmCount}</div>
-          <div style="color:#7f1d1d;font-size:12px;margin-top:4px">results — scanned every profile for the exact phrase</div>
-        </div>
-        <div style="background:#0a1a0f;border:1px solid #166534;border-radius:8px;padding:16px">
-          <div style="color:#34d399;font-size:11px;text-transform:uppercase;letter-spacing:1px;margin-bottom:8px">AI Vector Search (understands meaning)</div>
-          <div style="color:#6ee7b7;font-size:32px;font-weight:800">${d.aiHits.length}</div>
-          <div style="color:#166534;font-size:12px;margin-top:4px">matches — found workers whose skills MEAN the same thing</div>
-          ${aiNames.map((w: any) => `
-            <div style="margin-top:8px;padding:6px 10px;background:#0d1a12;border-radius:4px;font-size:11px">
-              <span style="color:#34d399;font-weight:600">${w.name}</span>
-              <span style="color:#64748b"> — ${w.role}${w.city ? ` in ${w.city}` : ""}</span>
-            </div>
-          `).join("")}
-        </div>
-      </div>
-    </div>`;
-    }).join("")}
-  </section>
-
-  <section style="margin:30px 0">
-    <h2 style="color:#e2e8f0;font-size:18px"><span style="color:#818cf8">Now combine both:</span> SQL precision + AI understanding</h2>
-    <p style="color:#94a3b8;font-size:13px;margin-bottom:16px">
-      The hybrid search runs a SQL filter (role, state, reliability) AND vector ranking together.
-      You get exact structural matches ranked by who's the best semantic fit — in one call.
-    </p>
-    <div style="margin-bottom:12px">
-      <span class="badge green">${hybrid.sql_matches?.toLocaleString()} workers match your filters</span>
-      <span class="badge purple">→ AI ranked the top ${hybrid.vector_reranked}</span>
-      <span class="badge blue">${tests[tests.length-1]?.ms}ms</span>
-    </div>
-    <table><thead><tr><th>ID</th><th>Name</th><th>Profile</th><th>AI Score</th><th>Verified</th></tr></thead>
-    <tbody>${workerRows}</tbody></table>
-    <p style="color:#475569;font-size:11px;margin-top:8px">Every result verified against the actual database. The AI cannot hallucinate workers that don't exist.</p>
-  </section>
-
-  <section>
-    <h2><span>03</span> Why This Matters — the numbers a CRM can't show you</h2>
-    <div style="display:grid;grid-template-columns:repeat(3,1fr);gap:16px">
-      <div class="stat">
-        <div class="num">${totalChunks.toLocaleString()}</div>
-        <div class="label">Text Chunks Vectorized</div>
-        <div style="color:#64748b;font-size:11px;margin-top:8px">Every worker's skills, certs, and history converted into searchable AI vectors by a LOCAL model. No cloud API. No per-query cost. Your data never leaves this server.</div>
-      </div>
-      <div class="stat">
-        <div class="num">0.98</div>
-        <div class="label">Search Accuracy</div>
-        <div style="color:#64748b;font-size:11px;margin-top:8px">98% recall — meaning 98 out of 100 truly relevant workers are found. Measured against brute-force ground truth on real embedded profiles.</div>
-      </div>
-      <div class="stat">
-        <div class="num">10M</div>
-        <div class="label">Vectors at 5ms</div>
-        <div style="color:#64748b;font-size:11px;margin-top:8px">Tested at 10 million vectors on disk. Search still takes 5ms. A traditional database would need minutes to full-text scan that volume.</div>
-      </div>
-    </div>
-  </section>
-
-  <section>
-    <h2><span>04</span> Local AI — your data, your models, your GPU</h2>
-    <p style="color:#94a3b8;font-size:13px">${g.name || "NVIDIA RTX A4000"} — ${g.used_mib || 0} / ${g.total_mib || 16376} MiB</p>
-    <div class="gpu-bar"><div class="gpu-fill" style="width:${((g.used_mib||0)/(g.total_mib||16376)*100)}%"></div></div>
-    <div class="g4" style="display:grid;grid-template-columns:repeat(4,1fr);gap:12px;margin-top:16px">
-      <div style="background:#111827;border-radius:8px;padding:12px;text-align:center">
-        <div style="color:#a78bfa;font-weight:700">qwen3</div>
-        <div style="color:#64748b;font-size:11px">8.2B · Reasoning</div>
-      </div>
-      <div style="background:#111827;border-radius:8px;padding:12px;text-align:center">
-        <div style="color:#60a5fa;font-weight:700">qwen2.5</div>
-        <div style="color:#64748b;font-size:11px">7B · Fast SQL</div>
-      </div>
-      <div style="background:#111827;border-radius:8px;padding:12px;text-align:center">
-        <div style="color:#34d399;font-weight:700">mistral</div>
-        <div style="color:#64748b;font-size:11px">7B · Generation</div>
-      </div>
-      <div style="background:#111827;border-radius:8px;padding:12px;text-align:center">
-        <div style="color:#fbbf24;font-weight:700">nomic</div>
-        <div style="color:#64748b;font-size:11px">137M · Embeddings</div>
-      </div>
-    </div>
-    <p style="color:#64748b;font-size:11px;margin-top:12px">Hot-swappable profiles. Switch between models in seconds. Each model specializes in what it's best at. No API keys, no usage fees, no data leaving the building.</p>
-  </section>
-
-  <div class="note">
-    <strong>Every number on this page runs LIVE.</strong> Hit refresh — the queries execute again on ${totalRows.toLocaleString()} real rows.
-    The AI vectors were generated by a local model running on the GPU above. No cloud APIs were used.
-    This is not a demo — this is the production system with real staffing data.
-  </div>
-
-  <div style="border-top:1px solid #1e293b;margin-top:40px;padding-top:40px">
-    <h2 style="border:none;font-size:22px;color:#f0f6fc;text-align:center;margin-bottom:8px">How This Actually Works</h2>
-    <p style="color:#94a3b8;text-align:center;font-size:14px;max-width:700px;margin:0 auto 30px">The technical architecture behind what you just saw — why it's different from a database, why your data never leaves this building, and how it handles millions of records.</p>
-
-    <div class="g2" style="display:grid;grid-template-columns:1fr 1fr;gap:20px;margin-bottom:30px">
-      <div style="background:#111827;border:1px solid #1e293b;border-radius:12px;padding:24px">
-        <div style="color:#f87171;font-size:12px;font-weight:600;text-transform:uppercase;letter-spacing:1px;margin-bottom:10px">Traditional CRM / Database</div>
-        <div style="color:#94a3b8;font-size:13px;line-height:1.8">
-          Stores records in rows and columns.<br>
-          Search = exact text matching ("forklift" finds "forklift").<br>
-          Can't understand that "warehouse help" = forklift operator.<br>
-          Slows down as data grows — millions of rows = slow queries.<br>
-          Every search is the same — doesn't learn or improve.<br>
-          Data lives on someone else's cloud server.
-        </div>
-      </div>
-      <div style="background:#111827;border:1px solid #1e293b;border-radius:12px;padding:24px">
-        <div style="color:#34d399;font-size:12px;font-weight:600;text-transform:uppercase;letter-spacing:1px;margin-bottom:10px">This System (Lakehouse)</div>
-        <div style="color:#94a3b8;font-size:13px;line-height:1.8">
-          AI reads every profile and <strong style="color:#e2e8f0">understands the meaning</strong>.<br>
-          Search = semantic understanding ("warehouse help" → finds loaders, forklift ops, shipping clerks).<br>
-          <strong style="color:#e2e8f0">Combines</strong> exact filters + AI ranking in one call.<br>
-          Tested at <strong style="color:#e2e8f0">10 million records at 5ms search</strong> — gets faster, not slower.<br>
-          Learns from successful placements — builds playbooks over time.<br>
-          <strong style="color:#e2e8f0">Runs entirely on hardware you own.</strong> Nothing leaves this server.
-        </div>
-      </div>
-    </div>
-
-    <div style="background:#0f172a;border:1px solid #1e293b;border-radius:12px;padding:30px;margin-bottom:24px">
-      <h3 style="color:#818cf8;font-size:16px;margin-bottom:16px">Your Data Never Leaves This Building</h3>
-      <div class="g3" style="display:grid;grid-template-columns:1fr 1fr 1fr;gap:16px">
-        <div>
-          <div style="color:#f0f6fc;font-weight:600;margin-bottom:6px">Local AI Models</div>
-          <div style="color:#94a3b8;font-size:12px">Four AI models run directly on your GPU — no OpenAI, no Google, no cloud API. Worker profiles, contracts, and communications never touch the internet. The AI that reads and understands your data lives on a machine you control.</div>
-        </div>
-        <div>
-          <div style="color:#f0f6fc;font-weight:600;margin-bottom:6px">Local Storage</div>
-          <div style="color:#94a3b8;font-size:12px">All data stored on S3-compatible object storage running on this server. Encrypted at rest. No third-party databases, no cloud subscriptions. If the internet goes down, this system keeps working — it doesn't depend on any external service.</div>
-        </div>
-        <div>
-          <div style="color:#f0f6fc;font-weight:600;margin-bottom:6px">Your Hardware</div>
-          <div style="color:#94a3b8;font-size:12px">${g.name || "NVIDIA RTX A4000"} GPU with ${g.total_mib || 16376} MB memory. 128 GB system RAM. All AI processing happens here. The cost is the hardware — no per-query fees, no per-user licenses, no monthly API bills that grow with usage.</div>
-        </div>
-      </div>
-    </div>
-
-    <div style="background:#111827;border:1px solid #1e293b;border-radius:12px;padding:30px;margin-bottom:24px">
-      <h3 style="color:#818cf8;font-size:16px;margin-bottom:16px">How It Handles Scale</h3>
-      <div style="color:#94a3b8;font-size:13px;line-height:1.8;margin-bottom:16px">
-        The system uses two search engines that work together — each handles what the other can't:
-      </div>
-      <div class="g2" style="display:grid;grid-template-columns:1fr 1fr;gap:16px;margin-bottom:16px">
-        <div style="background:#0d1117;border-radius:8px;padding:16px">
-          <div style="color:#58a6ff;font-weight:600;margin-bottom:6px">HNSW (In-Memory)</div>
-          <div style="color:#94a3b8;font-size:12px">Keeps frequently-used worker profiles in RAM for instant search. Under 1 millisecond response. Perfect for your active pool of workers — up to 5 million profiles in memory at once. 98% search accuracy.</div>
-        </div>
-        <div style="background:#0d1117;border-radius:8px;padding:16px">
-          <div style="color:#a78bfa;font-weight:600;margin-bottom:6px">Lance (On-Disk)</div>
-          <div style="color:#94a3b8;font-size:12px">For massive archives — 10 million+ records stored on disk. 5ms search speed. When your database grows past what fits in memory, Lance takes over automatically. No performance cliff. 94% search accuracy. New data appends in milliseconds without rebuilding the index.</div>
-        </div>
-      </div>
-      <div style="color:#64748b;font-size:12px;font-style:italic">The system automatically uses the right engine for each query. You never have to think about it — it's like having a fast filing cabinet and a massive warehouse that work together seamlessly.</div>
-    </div>
-
-    <div style="background:#111827;border:1px solid #1e293b;border-radius:12px;padding:30px;margin-bottom:24px">
-      <h3 style="color:#818cf8;font-size:16px;margin-bottom:16px">Hot-Swap Profiles — Different AI for Different Jobs</h3>
-      <div style="color:#94a3b8;font-size:13px;line-height:1.8;margin-bottom:16px">
-        The system runs multiple AI models and switches between them in seconds depending on the task. Like having specialists on call — each one is best at something different.
-      </div>
-      <div class="g4" style="display:grid;grid-template-columns:repeat(4,1fr);gap:12px">
-        <div style="background:#0d1117;border-radius:8px;padding:12px;text-align:center">
-          <div style="color:#a78bfa;font-weight:700;font-size:14px">Qwen 3</div>
-          <div style="color:#64748b;font-size:10px;margin-top:4px">Reasoning & analysis. Understands complex requests. 40,000 word context.</div>
-        </div>
-        <div style="background:#0d1117;border-radius:8px;padding:12px;text-align:center">
-          <div style="color:#60a5fa;font-weight:700;font-size:14px">Qwen 2.5</div>
-          <div style="color:#64748b;font-size:10px;margin-top:4px">Fast structured queries. Generates database searches from plain English.</div>
-        </div>
-        <div style="background:#0d1117;border-radius:8px;padding:12px;text-align:center">
-          <div style="color:#34d399;font-weight:700;font-size:14px">Mistral</div>
-          <div style="color:#64748b;font-size:10px;margin-top:4px">Writing & communication. Drafts personalized outreach messages.</div>
-        </div>
-        <div style="background:#0d1117;border-radius:8px;padding:12px;text-align:center">
-          <div style="color:#fbbf24;font-weight:700;font-size:14px">Nomic</div>
-          <div style="color:#64748b;font-size:10px;margin-top:4px">Reads profiles & understands meaning. Powers the semantic search.</div>
-        </div>
-      </div>
-      <div style="color:#64748b;font-size:12px;margin-top:12px;font-style:italic">When you switch tasks — from finding workers to drafting messages to analyzing trends — the system loads the right AI model automatically. Only one model uses the GPU at a time, so there's no performance penalty.</div>
-    </div>
-
-    <div style="background:#111827;border:1px solid #1e293b;border-radius:12px;padding:30px;margin-bottom:24px">
-      <h3 style="color:#818cf8;font-size:16px;margin-bottom:16px">Starting From Scratch — No Data Required</h3>
-      <div style="color:#94a3b8;font-size:13px;line-height:1.8;margin-bottom:16px">
-        <strong style="color:#f0f6fc">You don't need rich profiles to start.</strong> The system works with whatever you have — even just a name and a phone number. Here's what happens as you use it:
-      </div>
-      <div style="margin-bottom:16px">
-        <div style="display:flex;gap:12px;align-items:flex-start;margin-bottom:16px">
-          <div style="background:#1e293b;color:#f0f6fc;width:32px;height:32px;border-radius:50%;display:flex;align-items:center;justify-content:center;font-weight:700;flex-shrink:0">1</div>
-          <div>
-            <div style="color:#f0f6fc;font-weight:600">Day 1 — Import what you have</div>
-            <div style="color:#94a3b8;font-size:12px">Upload a spreadsheet with names, phone numbers, and roles. That's enough. The system organizes them by role and location so you can find who you need faster than scrolling a list. No scores, no metrics — just organized contacts.</div>
-          </div>
-        </div>
-        <div style="display:flex;gap:12px;align-items:flex-start;margin-bottom:16px">
-          <div style="background:#1e293b;color:#f0f6fc;width:32px;height:32px;border-radius:50%;display:flex;align-items:center;justify-content:center;font-weight:700;flex-shrink:0">2</div>
-          <div>
-            <div style="color:#f0f6fc;font-weight:600">Week 1 — You work, it watches</div>
-            <div style="color:#94a3b8;font-size:12px">Every placement you make, every timesheet that comes in, every call you log — the system records it. Not extra data entry — you're already doing this work. The system just starts keeping track. After a week, it knows which workers showed up on time and which didn't.</div>
-          </div>
-        </div>
-        <div style="display:flex;gap:12px;align-items:flex-start;margin-bottom:16px">
-          <div style="background:#1e293b;color:#f0f6fc;width:32px;height:32px;border-radius:50%;display:flex;align-items:center;justify-content:center;font-weight:700;flex-shrink:0">3</div>
-          <div>
-            <div style="color:#f0f6fc;font-weight:600">Month 1 — The AI starts helping</div>
-            <div style="color:#94a3b8;font-size:12px">Enough data has accumulated that reliability scores become meaningful. "Based on 8 placements, this worker has 95% reliability." The system starts suggesting matches you might have missed — workers you forgot about who are perfect for today's contract.</div>
-          </div>
-        </div>
-        <div style="display:flex;gap:12px;align-items:flex-start">
-          <div style="background:#7c3aed;color:#fff;width:32px;height:32px;border-radius:50%;display:flex;align-items:center;justify-content:center;font-weight:700;flex-shrink:0">→</div>
-          <div>
-            <div style="color:#f0f6fc;font-weight:600">The data you saw in the demo above?</div>
-            <div style="color:#94a3b8;font-size:12px">That's what the system looks like after it's been running. Rich profiles, reliability scores, certification tracking, intelligent matching — all built from the same work your staff already does. The difference between "Day 1" and "full intelligence" isn't a massive data migration. It's just time and normal operations.</div>
-          </div>
-        </div>
-      </div>
-    </div>
-
-    <div style="background:#0f172a;border:1px solid #7c3aed;border-radius:12px;padding:30px;margin-bottom:24px">
-      <h3 style="color:#a78bfa;font-size:16px;margin-bottom:12px">What the System Remembers (and Why It Matters)</h3>
-      <div style="color:#c4b5fd;font-size:13px;line-height:1.8;margin-bottom:16px">
-        Every successful operation becomes a <strong>playbook entry</strong> — a record of what worked. When a similar situation comes up, the system doesn't start from scratch. It checks: "Last time we needed welders in Ohio, here's who we placed and how it went."
-      </div>
-      <div style="color:#94a3b8;font-size:12px">
-        This is the fundamental difference from a CRM. A CRM stores data. This system stores <em>decisions and outcomes</em>. Over time, it becomes an institutional memory that doesn't retire, doesn't forget, and doesn't depend on one person knowing everything. Your senior staff's expertise becomes embedded in the system — not replacing them, but making sure what they know is available even when they're not in the room.
-      </div>
-    </div>
-
-    <div style="background:#111827;border:1px solid #1e293b;border-radius:12px;padding:30px">
-      <h3 style="color:#818cf8;font-size:16px;margin-bottom:16px">Measured, Not Promised</h3>
-      <table style="width:100%;font-size:13px;border-collapse:collapse">
-        <thead><tr><th style="text-align:left;padding:8px;color:#8b949e;border-bottom:1px solid #1e293b">Capability</th><th style="text-align:right;padding:8px;color:#8b949e;border-bottom:1px solid #1e293b">Measured</th><th style="text-align:left;padding:8px;color:#8b949e;border-bottom:1px solid #1e293b">What It Means</th></tr></thead>
-        <tbody>
-          <tr><td style="padding:8px;border-bottom:1px solid #1e293b">Search 500K workers</td><td style="padding:8px;text-align:right;color:#34d399;border-bottom:1px solid #1e293b">341ms avg</td><td style="padding:8px;color:#94a3b8;border-bottom:1px solid #1e293b">Results before you finish typing</td></tr>
-          <tr><td style="padding:8px;border-bottom:1px solid #1e293b">SQL query on 3M rows</td><td style="padding:8px;text-align:right;color:#34d399;border-bottom:1px solid #1e293b">sub-100ms</td><td style="padding:8px;color:#94a3b8;border-bottom:1px solid #1e293b">Any analytical question answered instantly</td></tr>
-          <tr><td style="padding:8px;border-bottom:1px solid #1e293b">10M vector search</td><td style="padding:8px;text-align:right;color:#34d399;border-bottom:1px solid #1e293b">5ms</td><td style="padding:8px;color:#94a3b8;border-bottom:1px solid #1e293b">Scale to 10 million profiles, still fast</td></tr>
-          <tr><td style="padding:8px;border-bottom:1px solid #1e293b">Search accuracy (HNSW)</td><td style="padding:8px;text-align:right;color:#34d399;border-bottom:1px solid #1e293b">98%</td><td style="padding:8px;color:#94a3b8;border-bottom:1px solid #1e293b">Finds 98 of 100 truly relevant workers</td></tr>
-          <tr><td style="padding:8px;border-bottom:1px solid #1e293b">Search accuracy (Lance)</td><td style="padding:8px;text-align:right;color:#34d399;border-bottom:1px solid #1e293b">94%</td><td style="padding:8px;color:#94a3b8;border-bottom:1px solid #1e293b">At 10M+ scale, still highly accurate</td></tr>
-          <tr><td style="padding:8px;border-bottom:1px solid #1e293b">Filter accuracy</td><td style="padding:8px;text-align:right;color:#34d399;border-bottom:1px solid #1e293b">100%</td><td style="padding:8px;color:#94a3b8;border-bottom:1px solid #1e293b">State, role, reliability filters are SQL-verified — never wrong</td></tr>
-          <tr><td style="padding:8px;border-bottom:1px solid #1e293b">Concurrent users</td><td style="padding:8px;text-align:right;color:#34d399;border-bottom:1px solid #1e293b">10+ simultaneous</td><td style="padding:8px;color:#94a3b8;border-bottom:1px solid #1e293b">Tested with 10 parallel queries in 82ms total</td></tr>
-          <tr><td style="padding:8px">Cloud dependency</td><td style="padding:8px;text-align:right;color:#34d399">Zero</td><td style="padding:8px;color:#94a3b8">Works offline. No internet required after setup.</td></tr>
-        </tbody>
-      </table>
-    </div>
-  </div>
-</div>
-<div class="footer">Lakehouse · ${totalChunks.toLocaleString()} AI-indexed profiles · 13 Rust modules · Built for staffing</div>
-</body></html>`;
-
-          return new Response(html, { headers: { ...cors, "Content-Type": "text/html" } });
         }
 
+
         // Proof JSON API (same data, no HTML)
         if (url.pathname === "/proof.json") {
           const ds = await api("GET", "/catalog/datasets") as any[];

mcp-server/proof.html

@@ -0,0 +1,453 @@
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+<div class="bar">
+  <h1>Lakehouse — Architecture &amp; Reproduction</h1>
+  <nav>
+    <a href=".">Dashboard</a>
+    <a href="console">Walkthrough</a>
+    <a href="proof" class="active">Architecture</a>
+  </nav>
+  <div class="rt" id="hdr-time">Running live tests…</div>
+</div>
+
+<div class="wrap">
+
+<div class="chapter">
+  <div class="num">Chapter 1</div>
+  <h2>Receipts, not promises</h2>
+  <div class="lede">Every test below ran live against the real gateway when you loaded this page. Sub-100ms SQL on multi-million-row Parquet, hybrid search with playbook boost applied. No fixtures. If a test fails, you'll see ✗.</div>
+  <div id="ch1-tests"><div class="loading">Running tests…</div></div>
+</div>
+
+<div class="chapter">
+  <div class="num">Chapter 2</div>
+  <h2>Architecture — 13 crates, one object store, one local AI runtime</h2>
+  <div class="lede">Request flows top to bottom. Every node is independently swappable. Every line is a real HTTP or gRPC hop that you can trace with <code>tcpdump</code>.</div>
+  <div class="card accent-b">
+    <pre>                            HTTP :3100  +  gRPC :3101
+                                    │
+                            ┌───────▼───────┐
+                            │   gateway     │   Rust · Axum · routing, CORS, auth, tools
+                            └───────┬───────┘
+           ┌────────────┬───────────┼───────────┬────────────┐
+           │            │           │           │            │
+      ┌────▼───┐   ┌────▼───┐  ┌────▼───┐  ┌────▼───┐   ┌────▼───┐
+      │catalog │   │ query  │  │ vector │  │ ingest │   │aibridge│
+      │   d    │   │   d    │  │   d    │  │   d    │   │        │
+      └────┬───┘   └────┬───┘  └────┬───┘  └────┬───┘   └────┬───┘
+           │            │           │           │            │
+           └────────────┴───────────┼───────────┴────────────┘
+                                    ▼
+                          ┌─────────────────┐
+                          │ object storage  │   Parquet files (local / S3)
+                          └─────────────────┘
+                                    ▲
+                                    │
+                            ┌───────┴────────┐
+                            │ Python sidecar │   FastAPI → Ollama
+                            │   (aibridge)   │   local models only
+                            └────────────────┘</pre>
+  </div>
+  <h3>Per-crate responsibility</h3>
+  <table class="plain">
+    <thead><tr><th>Crate</th><th>Role</th><th>Path</th></tr></thead>
+    <tbody>
+      <tr><td>shared</td><td>Types, errors, Arrow helpers, PII detection, secrets provider</td><td>crates/shared/</td></tr>
+      <tr><td>storaged</td><td>object_store I/O, BucketRegistry (multi-bucket), AppendLog, ErrorJournal</td><td>crates/storaged/</td></tr>
+      <tr><td>catalogd</td><td>Metadata authority — manifests, views, tombstones, profiles, schema fingerprints</td><td>crates/catalogd/</td></tr>
+      <tr><td>queryd</td><td>DataFusion SQL engine, MemTable cache, delta merge-on-read, compaction</td><td>crates/queryd/</td></tr>
+      <tr><td>ingestd</td><td>CSV/JSON/PDF(+OCR)/Postgres/MySQL ingest, cron schedules, auto-PII</td><td>crates/ingestd/</td></tr>
+      <tr><td>vectord</td><td>Embeddings as Parquet, HNSW, trial system, autotune agent, playbook_memory</td><td>crates/vectord/</td></tr>
+      <tr><td>vectord-lance</td><td>Firewall crate — Lance 4.0 + Arrow 57 isolated from main Arrow 55</td><td>crates/vectord-lance/</td></tr>
+      <tr><td>journald</td><td>Append-only mutation event log for time-travel &amp; audit</td><td>crates/journald/</td></tr>
+      <tr><td>aibridge</td><td>Rust↔Python sidecar, Ollama HTTP client, VRAM introspection</td><td>crates/aibridge/</td></tr>
+      <tr><td>gateway</td><td>Axum HTTP :3100 + gRPC :3101, middleware, tools registry</td><td>crates/gateway/</td></tr>
+      <tr><td>ui</td><td>Dioxus WASM internal developer UI</td><td>crates/ui/</td></tr>
+      <tr><td>mcp-server</td><td>Bun TypeScript recruiter-facing app (this server)</td><td>mcp-server/</td></tr>
+    </tbody>
+  </table>
+  <div class="ref"><strong>Source:</strong> git.agentview.dev/profit/lakehouse &nbsp;·&nbsp; <strong>ADRs:</strong> docs/DECISIONS.md (currently 20 records)</div>
+</div>
+
+<div class="chapter">
+  <div class="num">Chapter 3</div>
+  <h2>Dual-agent recursive consensus loop</h2>
+  <div class="lede">The system we use to execute staffing fills is a dual-agent recursive protocol. Two agents with distinct roles iterate against a shared log until one of three terminal states is reached. It is deterministic in structure, stochastic in content, and verifiable through the per-run log artifact.</div>
+  <h3>Agents and protocol</h3>
+  <div class="card accent-a">
+    <pre>  task in
+    │
+    ▼
+  ┌───────────────────────────────────────────────────────────┐
+  │  EXECUTOR (mistral:latest)                                │
+  │  ──────────────────────────────────────────────────────── │
+  │  input:   task spec + shared log + seen-candidates ledger │
+  │  output:  one JSON action per turn                        │
+  │             · {kind:"plan",steps:[…]}                     │
+  │             · {kind:"tool_call",tool,args,rationale}      │
+  │             · {kind:"propose_done",fills:[N of N]}        │
+  └───────────┬───────────────────────────────┬───────────────┘
+              │ tool_call                     │ propose_done
+              ▼                               │
+  ┌──────────────────────────┐                │
+  │  TOOL DISPATCH           │                │
+  │  hybrid_search / sql     │                │
+  │  (against live gateway)  │                │
+  └──────────┬───────────────┘                │
+             │ result (trimmed, exclusions)   │
+             ▼                                ▼
+  ┌───────────────────────────────────────────────────────────┐
+  │  REVIEWER (qwen2.5:latest)                                │
+  │  ──────────────────────────────────────────────────────── │
+  │  input:   task spec + shared log (including tool result)  │
+  │  output:  {kind:"critique",verdict:"continue|drift|       │
+  │                                    approve_done",notes}   │
+  └───────────┬───────────────────────────────────────────────┘
+              │
+        ┌─────┴─────┐
+        ▼           ▼           ▼
+    continue     drift       approve_done + propose_done ⟹ SEAL
+    (next turn)  (cap ≈ 3 →
+                  hard abort)
+    </pre>
+  </div>
+  <div class="ref"><strong>Code:</strong> tests/multi-agent/agent.ts (protocol + prompts) &nbsp;·&nbsp; tests/multi-agent/orchestrator.ts (run loop) &nbsp;·&nbsp; tests/multi-agent/scenario.ts (5-event warehouse week)</div>
+
+  <h3>Why "dual" — role specialization</h3>
+  <div class="narr">
+    <strong>The executor is an optimist.</strong> Its job is to produce progress: pull candidates, verify SQL, propose consensus. It's instructed to be decisive.
+    <br><br>
+    <strong>The reviewer is a pessimist.</strong> Its job is to catch drift: proposals that don't match the task's geography, fill count, or role. It's authorized to stop the loop.
+    <br><br>
+    This adversarial separation is cheaper and more deterministic than asking a single model to self-critique. The reviewer has a hard rule: on the turn after a <code>propose_done</code>, it MUST emit either <code>approve_done</code> or <code>drift</code> — it cannot stall with <code>continue</code>.
+  </div>
+
+  <h3>Why "parallel" — orchestrator can fan out</h3>
+  <div class="narr">
+    <strong>Independent pairs run concurrently.</strong> <code>tests/multi-agent/run_e2e_rated.ts</code> runs two task-specific agent pairs via <code>Promise.all</code>. Ollama serializes inference at the model level, so "parallel" is concurrent orchestration — but the substrate (gateway, queryd, vectord) handles concurrent requests cleanly. Verified in the scenario harness: two contracts sealing simultaneously.
+  </div>
+
+  <h3>Why "recursive" — each seal feeds the next</h3>
+  <div class="narr">
+    <strong>Consensus does not end at the sealed playbook.</strong> Every sealed playbook is persisted to <code>playbook_memory</code> via <code>POST /vectors/playbook_memory/seed</code>. The next hybrid search for a semantically similar operation consults that memory via <code>compute_boost_for(query_embedding, top_k, base_weight)</code> and re-ranks the candidate pool. The system builds on itself turn over turn, playbook over playbook.
+  </div>
+
+  <h3>Termination guarantees</h3>
+  <div class="math">
+    <span class="c">// three paths out, every run has one of these:</span><br>
+    sealed = executor.propose_done ∧ reviewer.approve_done ∧ fills.count == target<br>
+    abort  = consecutive_tool_errors ≥ MAX_TOOL_ERRORS (3) &nbsp;&nbsp;<span class="c">// executor can't form a valid call</span><br>
+    abort  = consecutive_drifts ≥ MAX_CONSECUTIVE_DRIFTS (3) &nbsp;<span class="c">// reviewer keeps flagging</span><br>
+    abort  = turn &gt; MAX_TURNS (12) &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;<span class="c">// no consensus reached in window</span>
+  </div>
+  <div class="narr">Every abort dumps the full log to <code>tests/multi-agent/playbooks/&lt;id&gt;-FAILED.json</code> for forensic review. No consensus is ever implicit.</div>
+</div>
+
+<div class="chapter">
+  <div class="num">Chapter 4</div>
+  <h2>Playbook memory — the compounding feedback loop</h2>
+  <div class="lede">A CRM stores events. This system turns events into re-ranking signal. Every sealed playbook endorses specific (worker, city, state) tuples. Every failure penalizes them. Every similar future query inherits the signal through cosine similarity.</div>
+
+  <h3>Seed shape</h3>
+  <div class="math">
+    PlaybookEntry {<br>
+    &nbsp;&nbsp;playbook_id,            <span class="c">// pb-seed-&lt;sha8&gt;</span><br>
+    &nbsp;&nbsp;operation,              <span class="c">// "fill: Welder x2 in Toledo, OH"</span><br>
+    &nbsp;&nbsp;approach, context,      <span class="c">// short canonical — long strings dilute embedding</span><br>
+    &nbsp;&nbsp;timestamp,              <span class="c">// RFC3339</span><br>
+    &nbsp;&nbsp;endorsed_names[],       <span class="c">// validated against workers_500k for city+state</span><br>
+    &nbsp;&nbsp;city, state,            <span class="c">// parsed from operation</span><br>
+    &nbsp;&nbsp;embedding               <span class="c">// 768-d nomic-embed-text of text shape</span><br>
+    }
+  </div>
+  <div class="ref"><strong>Code:</strong> crates/vectord/src/playbook_memory.rs (PlaybookEntry, FailureRecord, PlaybookMemoryState)</div>
+
+  <h3>Boost math (positive + decay + negative)</h3>
+  <div class="math">
+    <span class="c">// For each playbook pb among top-K most cosine-similar:</span><br>
+    <span class="c">// given query embedding qv, constant base_weight, n_workers = |pb.endorsed_names|</span><br>
+    <br>
+    similarity  = cosine(qv, pb.embedding) &nbsp;&nbsp; <span class="c">// skip if ≤ 0.05</span><br>
+    age_days    = (now - pb.timestamp) / 86_400 seconds<br>
+    decay       = e<sup>-age_days / 30</sup> &nbsp;&nbsp;<span class="c">// half-life = 30 days</span><br>
+    <br>
+    <span class="c">// For each endorsed worker in pb:</span><br>
+    key         = (pb.city, pb.state, name)<br>
+    fail_count  = failures[key] &nbsp;&nbsp;<span class="c">// # times this worker was marked no-show for same geo</span><br>
+    penalty     = 0.5<sup>min(fail_count, 20)</sup><br>
+    <br>
+    per_worker  = similarity × base_weight × decay × penalty / n_workers<br>
+    boost[key] = min(boost[key] + per_worker, MAX_BOOST_PER_WORKER)<br>
+    <br>
+    <span class="c">// MAX_BOOST_PER_WORKER = 0.25 — cap stops one popular worker from always winning</span>
+  </div>
+  <div class="ref"><strong>Code:</strong> crates/vectord/src/playbook_memory.rs::compute_boost_for &nbsp;·&nbsp; constants: MAX_BOOST_PER_WORKER, DEFAULT_TOP_K_PLAYBOOKS, BOOST_HALF_LIFE_DAYS</div>
+
+  <h3>Application at query time</h3>
+  <div class="math">
+    <span class="c">// In /vectors/hybrid handler (crates/vectord/src/service.rs):</span><br>
+    1. SQL filter narrows workers_500k to geo/role/availability<br>
+    2. Vector index returns top_k × 5 candidates by cosine to question<br>
+    3. compute_boost_for(qv, k=200) returns boost map<br>
+    4. For each candidate: parse (name, city, state) from chunk, look up boost, add to score<br>
+    5. Re-sort sources by boosted score<br>
+    6. Truncate to requested top_k, return with playbook_boost and playbook_citations
+  </div>
+  <div class="narr">
+    <strong>Why k=200.</strong> Direct measurement showed cosine similarity clusters in the 0.55-0.67 band across all playbooks regardless of geo (nomic-embed-text has narrow discrimination on this kind of structured operation text). A k of 25 silently missed geo-matched playbooks. k=200 is the measured floor for reliably catching compounding. Brute-force over 200 × 768-d is sub-ms even on this hardware.
+  </div>
+  <div class="ref"><strong>Evidence:</strong> Chicago Electrician compounding test 2026-04-20 — Carmen Green, Anna Patel, Fatima Wilson went from rank &gt;5 / boost 0 / 0 citations (run 0, no seed) to rank 1/2/3 / boost +0.250 (capped) / 3 citations each (run 3, after 3 identical seeds). Each seed increments citations; total boost caps at 0.25/worker.</div>
+
+  <h3>Write-through to SQL</h3>
+  <div class="narr">
+    <strong>successful_playbooks_live</strong> is a DataFusion-queryable Parquet surface maintained by <code>POST /vectors/playbook_memory/persist_sql</code>. Every <code>/log</code> from the recruiter UI triggers seed → persist_sql. The in-memory store and the SQL surface stay synchronized (full snapshot on each persist, safe because memory is source of truth).
+  </div>
+  <div class="ref"><strong>Code:</strong> crates/vectord/src/playbook_memory.rs::persist_to_sql &nbsp;·&nbsp; catalog-registered under "successful_playbooks_live"</div>
+
+  <h3>Pattern discovery (Path 2 — meta-index)</h3>
+  <div class="narr">
+    <strong>Beyond "who was endorsed."</strong> <code>POST /vectors/playbook_memory/patterns</code> takes a query, finds top-K similar past playbooks, pulls each endorsed worker's full workers_500k profile, and aggregates shared traits: recurring certifications, skill frequencies, modal archetype, reliability distribution. Returns a <code>discovered_pattern</code> string showing operator-actionable signal the user didn't explicitly query for.
+  </div>
+  <div class="ref"><strong>Code:</strong> crates/vectord/src/playbook_memory.rs::discover_patterns &nbsp;·&nbsp; <strong>Surfaces:</strong> /vectors/playbook_memory/patterns endpoint, /intelligence/chat response, /intelligence/permit_contracts cards</div>
+</div>
+
+<div class="chapter">
+  <div class="num">Chapter 5</div>
+  <h2>Key architectural choices — what was picked and why</h2>
+  <div class="lede">Each choice is documented in <code>docs/DECISIONS.md</code> (Architecture Decision Records). If you dispute any of these, the ADR names the alternatives we rejected and the measurement that drove the call.</div>
+  <div class="card">
+    <div class="row accent-b">
+      <div style="flex:1"><div class="title">ADR-001 · Object storage as source of truth</div><div class="meta">No traditional database. All data is Parquet on S3-compatible object storage. Eliminates DB operational overhead; every engine can read Parquet.</div></div>
+    </div>
+    <div class="row accent-b">
+      <div style="flex:1"><div class="title">ADR-008 · Embeddings stored as Parquet, not a vector DB</div><div class="meta">Keeps all data in one portable format. No Pinecone/Weaviate/Qdrant lock-in. Trade-off: brute-force search up to ~100K; HNSW beyond.</div></div>
+    </div>
+    <div class="row accent-l">
+      <div style="flex:1"><div class="title">ADR-012 · Append-only event journal — never destroy evidence</div><div class="meta">Every mutation is appended. Compliance, audit, AI-decision forensics. Impossible to retrofit; easy to add now.</div></div>
+    </div>
+    <div class="row accent-a">
+      <div style="flex:1"><div class="title">ADR-015 · Tool registry before raw SQL for agents</div><div class="meta">Named, governed, audited actions for agents. Permission checks, rate limits, parameter validation. MCP-compatible.</div></div>
+    </div>
+    <div class="row accent-w">
+      <div style="flex:1"><div class="title">ADR-019 · Hybrid Parquet+HNSW ⊕ Lance vector backend</div><div class="meta">Parquet+HNSW primary (2.55× faster search at 100K). Lance secondary for index-build speed (14× faster), random fetch (112× faster), append (structural). Per-profile <code>vector_backend: Parquet | Lance</code>.</div></div>
+    </div>
+    <div class="row accent-r">
+      <div style="flex:1"><div class="title">ADR-020 · Idempotent register() with schema-fingerprint gate</div><div class="meta">Same (name, fingerprint) reuses manifest. Different fingerprint = 409 Conflict. Prevents silent duplicate manifests. Cleanup run collapsed 374 → 31 datasets.</div></div>
+    </div>
+    <div class="row accent-l">
+      <div style="flex:1"><div class="title">Phase 19 design note · Statistical + semantic, not neural</div><div class="meta">Meta-index is cosine similarity + endorsement aggregation. No model training. Rebuildable from <code>successful_playbooks</code> alone. Neural re-ranker deferred to Phase 20+ only if statistical floor plateaus.</div></div>
+    </div>
+  </div>
+</div>
+
+<div class="chapter">
+  <div class="num">Chapter 6</div>
+  <h2>Measured at scale, on this machine</h2>
+  <div class="lede">Hardware: i9 + 128GB RAM + Nvidia A4000 16GB VRAM. Numbers below are from <em>this</em> running instance. Refresh the page and they'll recompute.</div>
+  <div class="grid" id="ch6-scale"><div class="loading">Loading scale data…</div></div>
+  <div id="ch6-recall" style="margin-top:10px"></div>
+</div>
+
+<div class="chapter">
+  <div class="num">Chapter 7</div>
+  <h2>Verify or dispute — reproduce it yourself</h2>
+  <div class="lede">Every claim below is a curl away from falsification.</div>
+  <div class="card">
+    <div class="narr"><strong>Health.</strong> Should return <code>lakehouse ok</code>.</div>
+    <pre>curl http://localhost:3100/health</pre>
+    <div class="narr"><strong>Any SQL on multi-million-row Parquet.</strong> Sub-100ms typical.</div>
+    <pre>curl -s -X POST http://localhost:3100/query/sql \
+  -H 'Content-Type: application/json' \
+  -d '{"sql":"SELECT role, COUNT(*) FROM workers_500k WHERE state=\"IL\" GROUP BY role LIMIT 5"}'</pre>
+    <div class="narr"><strong>Hybrid search with playbook boost.</strong> The whole Phase 19 feedback loop in one request.</div>
+    <pre>curl -s -X POST http://localhost:3100/vectors/hybrid \
+  -H 'Content-Type: application/json' \
+  -d '{"index_name":"workers_500k_v1",
+       "sql_filter":"role = '\''Forklift Operator'\'' AND city = '\''Chicago'\'' AND CAST(availability AS DOUBLE) > 0.5",
+       "question":"reliable forklift operator",
+       "top_k":5,"use_playbook_memory":true,"playbook_memory_k":200}'</pre>
+    <div class="narr"><strong>Playbook memory stats.</strong> Count + endorsed names + sample.</div>
+    <pre>curl http://localhost:3100/vectors/playbook_memory/stats</pre>
+    <div class="narr"><strong>Pattern discovery.</strong> What do past similar fills have in common?</div>
+    <pre>curl -s -X POST http://localhost:3100/vectors/playbook_memory/patterns \
+  -H 'Content-Type: application/json' \
+  -d '{"query":"Forklift Operator in Chicago, IL","top_k_playbooks":25,"min_trait_frequency":0.3}'</pre>
+    <div class="narr"><strong>Run the dual-agent scenario yourself.</strong> All 5 events, real fills, real artifacts.</div>
+    <pre>cd /home/profit/lakehouse
+bun run tests/multi-agent/scenario.ts
+# Output: tests/multi-agent/playbooks/scenario-&lt;timestamp&gt;/report.md</pre>
+  </div>
+</div>
+
+<div class="chapter">
+  <div class="num">Chapter 8</div>
+  <h2>What we are <em>not</em> claiming</h2>
+  <div class="lede">Every impressive-sounding number comes with a footnote. Here are the honest limits.</div>
+  <div class="card">
+    <div class="row accent-a"><div style="flex:1"><div class="title">workers_500k is synthetic.</div><div class="meta">Real client ATS export replaces this table. Schema is deliberately identical to a production ATS.</div></div></div>
+    <div class="row accent-a"><div style="flex:1"><div class="title">candidates table has 1,000 rows.</div><div class="meta">Intentionally small for demo. call_log references higher candidate_ids that don't cross-reference — this is a dataset alignment issue, not a pipeline issue.</div></div></div>
+    <div class="row accent-b"><div style="flex:1"><div class="title">Chicago permit data is real.</div><div class="meta">Pulled live from data.cityofchicago.org/resource/ydr8-5enu.json (Socrata API). Not synthetic. Not cached.</div></div></div>
+    <div class="row accent-l"><div style="flex:1"><div class="title">Playbook memory is seeded from demo runs.</div><div class="meta">The pipeline that seeds it is identical to what a live recruiter would trigger via /log. Same code path.</div></div></div>
+    <div class="row accent-w"><div style="flex:1"><div class="title">Local 7B models (mistral, qwen2.5) are imperfect.</div><div class="meta">They occasionally malform tool calls or drop fields. Multi-agent scenarios seal roughly 40-80% in one run. Larger models or constrained decoding would improve this. Not a substrate problem.</div></div></div>
+    <div class="row accent-r"><div style="flex:1"><div class="title">No rate/margin awareness yet.</div><div class="meta">Worker pay expectations vs contract bill rates are not modeled. Flagged as a Phase 20 item; no architectural blocker.</div></div></div>
+  </div>
+</div>
+
+</div>
+
+<div class="footer">Lakehouse · Architecture page regenerates tests on every load · <a href="console">walkthrough</a> · <a href=".">dashboard</a></div>
+
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+    addStat(host6,(scale.total_chunks||0).toLocaleString(),'Vector chunks indexed','across '+(scale.indexes||0)+' HNSW + Lance indexes','accent-a');
+    var gpu=r.gpu||{};
+    var vramStr=(gpu.vram_used_mib!==undefined?gpu.vram_used_mib+' / '+gpu.vram_total_mib+' MiB':'—');
+    addStat(host6,vramStr,'GPU VRAM',(gpu.name||'A4000'),'accent-w');
+    if(r.recall){
+      var reco=r.recall||{};
+      var hnswStr=(reco.hnsw!==undefined?(reco.hnsw*100).toFixed(0)+'%':'—');
+      addStat(host6,hnswStr,'HNSW recall','measured · '+(reco.note||''),'accent-l');
+    }
+
+    var host6r=document.getElementById('ch6-recall');host6r.textContent='';
+    if(r.lance_10m){
+      var narr=el('div','narr');
+      narr.appendChild(el('strong',null,'Scale headroom: '));
+      narr.appendChild(document.createTextNode(
+        'Lance backend tested at '+r.lance_10m.vectors.toLocaleString()+' vectors, '
+        +r.lance_10m.disk_gb+' GB on disk, '+r.lance_10m.search_p50_ms+'ms p50. '
+        +(r.lance_10m.note||'')
+      ));
+      host6r.appendChild(narr);
+    }
+
+    document.getElementById('hdr-time').textContent='Generated · '+(r.generated?new Date(r.generated).toLocaleTimeString():'live');
+  }).catch(function(e){
+    var host1=document.getElementById('ch1-tests');host1.textContent='';
+    host1.appendChild(el('div','err','Live tests failed: '+(e.message||e)+'. Server may be offline.'));
+  });
+}
+
+function addStat(host,n,l,sub,cls){
+  var d=el('div','card stat-lg '+(cls||''));
+  d.appendChild(el('div','n',n));
+  d.appendChild(el('div','l',l));
+  d.appendChild(el('div','sub',sub||''));
+  host.appendChild(d);
+}
+</script>
+</body></html>