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/serving/main.py

@@ -196,6 +196,9 @@ class AgentComputeRequest(BaseModel):
     now_iso: Optional[str] = None  # ISO 8601; defaults to utcnow
     # Per-agent prefs from user_preferences (merged: user source overrides inferred).
     agent_prefs: dict = {}
+    # Pre-fetched enrichment cache: {content_hash -> description}. Avoids re-calling
+    # LiteLLM for task titles already expanded in a prior compute cycle.
+    enrichment_cache: dict[str, str] = {}
 
 
 class AgentComputeResponse(BaseModel):
@@ -206,6 +209,8 @@ class AgentComputeResponse(BaseModel):
     computed_at: str
     expires_at: str
     agent_version: str
+    # New enrichments generated during this compute cycle; caller persists to DB.
+    new_enrichments: dict[str, str] = {}
 
 
 class AgentInferRequest(BaseModel):
@@ -314,6 +319,7 @@ async def compute_agent(agent_id: str, req: AgentComputeRequest) -> AgentCompute
         feedback_history=req.feedback_history,
         now=now,
         agent_prefs=req.agent_prefs,
+        enrichment_cache=req.enrichment_cache,
     )
     try:
         output = agent.compute(inp)
@@ -321,6 +327,8 @@ async def compute_agent(agent_id: str, req: AgentComputeRequest) -> AgentCompute
         log.error("agent_compute_failed", agent_id=agent_id, user_id=req.user_id, error=str(exc))
         raise HTTPException(status_code=500, detail=f"Agent compute failed: {exc}")
 
+    new_enrichments: dict[str, str] = output.signals_snapshot.pop("_new_enrichments", {})
+
     log.info("agent_computed", agent_id=agent_id, user_id=req.user_id, expires_at=output.expires_at)
     span = _start_span(
         f"compute:{agent_id}",
@@ -339,6 +347,7 @@ async def compute_agent(agent_id: str, req: AgentComputeRequest) -> AgentCompute
         computed_at=output.computed_at,
         expires_at=output.expires_at,
         agent_version=output.agent_version,
+        new_enrichments=new_enrichments,
     )