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/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');
       }
     }
   }