feat: add AI Digital Employee agent orchestrator with pipeline tracking

- New AgentPipeline model with JSONB pipeline_data for stages/leads/summary
- AgentOrchestrator service chains DiscoveryService search→analyze→outreach→auto-save
- 3 new API endpoints: POST /agent/start, GET /agent/pipelines, GET /agent/{id}
- Full Agent dashboard Vue component with stats, pipeline grid, leads table, outreach preview
- Sidebar redesigned with AI Agent as primary entry point
- Updated PROGRESS.md, AGENTS.md, DATABASE_SCHEMA.md with latest state

backend/app/models/agent_pipeline.py

@@ -0,0 +1,30 @@
+from sqlalchemy import Column, String, Integer, DateTime, Text
+from sqlalchemy.dialects.postgresql import UUID, JSONB
+from datetime import datetime
+from app.database import Base
+import uuid
+
+
+class AgentPipeline(Base):
+    __tablename__ = "agent_pipelines"
+
+    id = Column(UUID(as_uuid=True), primary_key=True, default=uuid.uuid4)
+    user_id = Column(UUID(as_uuid=True), nullable=False, index=True)
+    status = Column(String(50), default="running")
+    progress = Column(Integer, default=0)
+    product_name = Column(String(255), nullable=False)
+    product_description = Column(Text, default="")
+    target_market = Column(String(255), nullable=False)
+    pipeline_data = Column(JSONB, default={
+        "stages": {
+            "discover": {"status": "pending", "message": ""},
+            "analyze": {"status": "pending", "message": ""},
+            "outreach": {"status": "pending", "message": ""},
+            "complete": {"status": "pending", "message": ""},
+        },
+        "leads": [],
+        "summary": {},
+    })
+    error_message = Column(Text)
+    created_at = Column(DateTime, default=datetime.utcnow)
+    updated_at = Column(DateTime, default=datetime.utcnow, onupdate=datetime.utcnow)