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/base.py

@@ -20,6 +20,9 @@ class AgentInput:
     # precedence over 'inferred' source; the caller resolves priority before
     # passing this dict in.
     agent_prefs: dict = field(default_factory=dict)
+    # Pre-fetched enrichment cache: {content_hash -> description}. Populated by
+    # the TS caller from the task_enrichments DB table to avoid redundant LLM calls.
+    enrichment_cache: dict = field(default_factory=dict)
 
 
 @dataclass