feat(clustering): persistent enrichment cache in task_enrichments table

Each unique task title is now enriched by LiteLLM once and cached in the DB.
Subsequent agent compute cycles (every 12h) fetch the cache before calling
ml-serving; only new titles hit the tip-generator.

- DB: task_enrichments(content_hash PK, description, model, created_at)
- TS: fetchEnrichmentCache / persistEnrichments helpers in agent-outputs.ts;
  enrichment_cache passed in compute request, new_enrichments persisted from response
- Python: AgentComputeRequest.enrichment_cache / AgentComputeResponse.new_enrichments;
  AgentInput.enrichment_cache; _enrich_batch returns (descriptions, new_entries);
  cluster_tasks returns (clusters, new_enrichments)
- FocusAreaAgent stashes new_enrichments in signals_snapshot under _new_enrichments;
  compute_agent endpoint pops it before storing the snapshot

Closes part of #129

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

ml/agents/tests/test_agents.py

@@ -245,8 +245,9 @@ class TestFocusAreaAgent:
 
     def test_snapshot_keys(self):
         out = self.agent.compute(_inp(tasks=[_task("T1", project_id="A")]))
+        public_keys = {k for k in out.signals_snapshot if not k.startswith("_")}
         assert {"top_cluster_label", "top_task_count", "top_overdue_count", "cluster_count",
-                "strategy", "preferred_areas"} == set(out.signals_snapshot)
+                "strategy", "preferred_areas"} == public_keys
 
 
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