feat(serving): replace MLflow run logging with native trace spans

Convert ml-serving from isolated MLflow runs to nested traces using
mlflow.start_span_no_context(). The recommend endpoint now emits a full
span tree: recommend (CHAIN) → build_context (TOOL), agent:* (AGENT) ×N,
llm_orchestrator (LLM). Compute and infer endpoints each emit a single span.

Supporting changes:
- mlflow-skinny>=3.1.0 added to requirements
- MLflow configured with --serve-artifacts + mlflow-artifacts:/ default root
  for cross-container artifact proxy (spans now persist from ml-serving)
- --allowed-hosts extended to include mlflow:5000 (SDK includes port in Host)
- science_destiny slider wired through prompts.py and recommend endpoint
- Config page exposes science/destiny slider (0=data-driven, 100=intuitive)
- Tip page shows rationale inline on tap

Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>

services/api/src/profile/__tests__/eligibility.test.ts

@@ -35,7 +35,7 @@ const AGENT_C = { ...MANIFEST_DEFAULTS, id: 'agent-c', required_consents: ['data
 beforeAll(async () => {
   await testDb.insert(users).values({
     id: 'u1', email: 'u@test.com', name: null, image: null, role: 'user',
-    consentGiven: false, createdAt: NOW,
+    createdAt: NOW,
   });
 });
 

services/api/src/routes/__tests__/recommender.test.ts

@@ -213,7 +213,7 @@ describe('POST /recommend integration', () => {
     });
 
     // Intercept the /recommend body to inspect what agent_outputs were sent
-    const origFetch = globalThis.fetch as ReturnType<typeof vi.fn>;
+    const origFetch = globalThis.fetch as unknown as (url: string, init?: RequestInit) => Promise<Response>;
     const wrappedFetch = vi.fn().mockImplementation(async (url: string, init?: RequestInit) => {
       if (String(url).includes('/recommend') && init?.body) {
         const body = JSON.parse(init.body as string);

services/api/src/routes/agent-outputs.ts

@@ -166,7 +166,7 @@ export async function computeAndStore(userId: string, agentId: string): Promise<
     method: 'POST',
     headers: { 'Content-Type': 'application/json' },
     body: JSON.stringify({ user_id: userId, tasks, profile, feedback_history: feedbackHistory, agent_prefs: agentPrefs }),
-    signal: AbortSignal.timeout(15_000),
+    signal: AbortSignal.timeout(60_000),
   });
 
   if (!mlResp.ok) {

services/api/src/routes/recommender.ts

@@ -2,7 +2,7 @@ import { type Router as ExpressRouter, Router, Response } from 'express';
 import { nanoid } from 'nanoid';
 import { logger } from '../logger.js';
 import { db } from '../db/index.js';
-import { integrationTokens, tipFeedback, tipViews, tipScores } from '../db/schema.js';
+import { integrationTokens, tipFeedback, tipViews, tipScores, userPreferences } from '../db/schema.js';
 import { eq, and, desc } from 'drizzle-orm';
 import { requireAuth, AuthenticatedRequest } from '../middleware/session.js';
 import { config } from '../config.js';
@@ -52,6 +52,16 @@ interface OrchestratorResult {
   agentIds: string[];
 }
 
+async function loadOrchestratorPref<T>(userId: string, key: string): Promise<T | undefined> {
+  const rows = await db
+    .select({ valueJson: userPreferences.valueJson })
+    .from(userPreferences)
+    .where(and(eq(userPreferences.userId, userId), eq(userPreferences.scope, 'orchestrator'), eq(userPreferences.key, key)))
+    .limit(1);
+  if (!rows.length) return undefined;
+  try { return JSON.parse(rows[0].valueJson) as T; } catch { return undefined; }
+}
+
 async function fetchOrchestratorTip(
   userId: string,
   signals: Signal[],
@@ -59,9 +69,10 @@ async function fetchOrchestratorTip(
   dayOfWeek: number,
   traceparent?: string,
 ): Promise<OrchestratorResult | null> {
-  const [allAgentRows, eligibleIds] = await Promise.all([
+  const [allAgentRows, eligibleIds, scienceDestiny] = await Promise.all([
     getActiveAgentOutputs(userId),
     getEligibleAgentIds(userId),
+    loadOrchestratorPref<number>(userId, 'science_destiny'),
   ]);
   const agentOutputs = allAgentRows
     .filter((r) => eligibleIds.has(r.agentId))
@@ -78,7 +89,7 @@ async function fetchOrchestratorTip(
     const res = await fetch(`${config.ML_SERVING_URL}/recommend`, {
       method: 'POST',
       headers: { 'Content-Type': 'application/json', ...(traceparent ? { traceparent } : {}) },
-      body: JSON.stringify({ user_id: userId, agent_outputs: agentOutputs, tasks, hour_of_day: hour, day_of_week: dayOfWeek }),
+      body: JSON.stringify({ user_id: userId, agent_outputs: agentOutputs, tasks, hour_of_day: hour, day_of_week: dayOfWeek, science_destiny: scienceDestiny ?? 50 }),
       signal: AbortSignal.timeout(15_000),
     });
     if (!res.ok) return null;

services/api/src/signals/agent-scheduler.ts

@@ -68,14 +68,13 @@ async function runCycle(agentIds: string[]): Promise<void> {
   let failed = 0;
 
   for (const userId of userIds) {
-    const results = await Promise.allSettled(
-      agentIds.map((agentId) => computeAndStore(userId, agentId)),
-    );
-    for (const r of results) {
-      if (r.status === 'fulfilled') ok++;
-      else {
+    for (const agentId of agentIds) {
+      try {
+        await computeAndStore(userId, agentId);
+        ok++;
+      } catch (err: any) {
         failed++;
-        logger.error({ err: r.reason, userId }, 'agent-scheduler: compute error');
+        logger.error({ err, userId, agentId }, 'agent-scheduler: compute error');
       }
     }
   }