diff --git a/salesflow-saas/backend/app/services/core_os/decision_memo.py b/salesflow-saas/backend/app/services/core_os/decision_memo.py
new file mode 100644
index 00000000..51babc79
--- /dev/null
+++ b/salesflow-saas/backend/app/services/core_os/decision_memo.py
@@ -0,0 +1,61 @@
+from pydantic import BaseModel, ConfigDict, Field
+from typing import List, Dict, Any, Optional
+import uuid
+from datetime import datetime
+
+class RiskRegisterItem(BaseModel):
+    risk: str
+    severity: str  # "high", "medium", "low", "critical"
+    mitigation: str
+
+class FinancialImpact(BaseModel):
+    revenue_upside_sar: float = 0.0
+    cost_downside_sar: float = 0.0
+    capital_at_risk_sar: float = 0.0
+
+class AuditMetadata(BaseModel):
+    verified: bool = False
+    tool_proof_id: Optional[str] = None
+    policy_check_passed: bool = False
+    agent_id: str
+    timestamp: str
+
+class DecisionMemo(BaseModel):
+    """
+    The Universal Output Contract (Decision Memo)
+    All Sovereign Growth OS Agents MUST return this exact schema.
+    """
+    model_config = ConfigDict(extra="allow")
+
+    memo_id: str = Field(default_factory=lambda: f"memo_{uuid.uuid4().hex[:10]}")
+    objective: str
+    decision_context: str
+    inputs_used: List[str]
+    assumptions: List[str]
+    recommendation_ar: str
+    alternatives_considered: List[str]
+    expected_financial_impact: FinancialImpact
+    risk_register: List[RiskRegisterItem]
+    confidence_score: float = Field(ge=0, le=100)
+    required_approvals: List[str]
+    next_best_action: str
+    rollback_plan: str
+    evidence_links: List[str]
+    audit_metadata: AuditMetadata
+
+    def to_json(self) -> Dict[str, Any]:
+        return self.model_dump()
+        
+    @classmethod
+    def create_memo(cls, agent_id: str, objective: str, recommendation: str, 
+                    confidence: float, **kwargs) -> 'DecisionMemo':
+        """Helper to safely instantiate memos with timestamps included."""
+        audit = AuditMetadata(
+            agent_id=agent_id,
+            timestamp=datetime.utcnow().isoformat()
+        )
+        kwargs["audit_metadata"] = audit
+        kwargs["objective"] = objective
+        kwargs["recommendation_ar"] = recommendation
+        kwargs["confidence_score"] = confidence
+        return cls(**kwargs)
diff --git a/salesflow-saas/backend/app/services/strategic_deals/partnership_scout.py b/salesflow-saas/backend/app/services/strategic_deals/partnership_scout.py
new file mode 100644
index 00000000..da7a134f
--- /dev/null
+++ b/salesflow-saas/backend/app/services/strategic_deals/partnership_scout.py
@@ -0,0 +1,86 @@
+import uuid
+from typing import Dict, Any, List
+# import langgraph primitives when integrated, for now structure the state and logic
+# from langgraph.graph import StateGraph, END
+
+class PartnershipScoutWorkflow:
+    """
+    Partnership Scout Agent (LangGraph-based state machine).
+    Reads market signals and generates detailed partnership scorecards.
+    """
+    
+    def __init__(self, memory_store, decision_engine):
+        self.memory_store = memory_store
+        self.decision_engine = decision_engine
+        
+    def _fetch_company_data(self, state: dict) -> dict:
+        signal = state.get("signal", {})
+        target = signal.get("company_name")
+        # In a real environment, call clearbit/linkedin/web-scraper here
+        state["enriched_data"] = {"name": target, "industry": "SaaS", "revenue_tier": "mid-market"}
+        return state
+
+    def _score_partnership_fit(self, state: dict) -> dict:
+        data = state.get("enriched_data", {})
+        score = 85 if data.get("industry") == "SaaS" else 40
+        state["fit_score"] = score
+        state["synergy_potential"] = ["Product reselling", "Data API sharing"]
+        return state
+
+    def _generate_decision_memo(self, state: dict) -> dict:
+        # Generate the structured Decision Memo
+        score = state.get("fit_score", 0)
+        target = state.get("enriched_data", {}).get("name", "Unknown")
+        
+        memo_kwargs = {
+            "decision_context": f"Market signal detected interest for {target}",
+            "inputs_used": ["LinkedIn API", "Crunchbase Signal"],
+            "assumptions": ["Revenue over 10M SAR", "No strict exclusivity clauses"],
+            "alternatives_considered": ["Ignore signal", "Direct M&A (ruled out due to size)"],
+            "expected_financial_impact": {
+                "revenue_upside_sar": 250000.0,
+                "cost_downside_sar": 15000.0,
+                "capital_at_risk_sar": 0.0
+            },
+            "risk_register": [
+                {"risk": "Brand overlap", "severity": "medium", "mitigation": "Co-branding guidelines"}
+            ],
+            "required_approvals": ["VP Partnerships"],
+            "next_best_action": "Send Partnership introductory email to C-level",
+            "rollback_plan": "Cease communications and mark as disqualified in CRM",
+            "evidence_links": ["https://crm.dealix.local/signals/1"]
+        }
+        
+        recommendation = f"Initiate Alliance Structuring with {target} (Score: {score})" if score > 70 else "Discard lead."
+        
+        memo = self.decision_engine.create_memo(
+            agent_id="partnership_scout",
+            objective="Evaluate partnership market fit",
+            recommendation=recommendation,
+            confidence=float(score),
+            **memo_kwargs
+        )
+        
+        state["final_memo"] = memo.to_json()
+        return state
+
+    def execute_flow(self, signal: Dict[str, Any]) -> Dict[str, Any]:
+        """
+        Simulates the LangGraph execution flow: fetch -> score -> memo.
+        """
+        state = {"signal": signal}
+        state = self._fetch_company_data(state)
+        state = self._score_partnership_fit(state)
+        state = self._generate_decision_memo(state)
+        
+        # Save to memory backbone
+        memo_id = self.memory_store.store_item(
+            domain="partners",
+            title=f"Partner Scout: {signal.get('company_name', 'Unknown')}",
+            memory_type="Partner Evaluation",
+            owner="VP Partnerships",
+            confidence=int(state.get("fit_score", 0)),
+            summary=state["final_memo"]["recommendation_ar"]
+        )
+        
+        return {"status": "scouted", "memo_id": memo_id, "score": state["fit_score"]}
diff --git a/salesflow-saas/backend/app/services/strategic_deals/strategic_pmo.py b/salesflow-saas/backend/app/services/strategic_deals/strategic_pmo.py
new file mode 100644
index 00000000..2ff84a45
--- /dev/null
+++ b/salesflow-saas/backend/app/services/strategic_deals/strategic_pmo.py
@@ -0,0 +1,79 @@
+from typing import Dict, Any, List
+from datetime import datetime, timedelta
+
+class StrategicPMOAgent:
+    """
+    Strategic PMO Agent for Sovereign OS.
+    Translates long-term strategies (e.g., Post-Merger Integration, Global Expansion)
+    into tracked workstreams, RAG statuses (Red/Amber/Green), and escalates blockers.
+    """
+    
+    def __init__(self, memory_store, decision_engine):
+        self.memory_store = memory_store
+        self.decision_engine = decision_engine
+        
+    def breakdown_initiative(self, initiative_title: str, goals: List[str], deadline_days: int) -> Dict[str, Any]:
+        """Takes an executive goal and breaks it down into tracks."""
+        # Simulated LLM generation of tasks based on the M&A DD or Expansion Playbook
+        tasks = [
+            {"task": "Legal Entity Setup", "owner": "Legal", "due_in": min(14, deadline_days), "status": "green"},
+            {"task": "Financial Integration", "owner": "Finance", "due_in": min(30, deadline_days), "status": "amber"},
+            {"task": "IT Systems Merge", "owner": "IT", "due_in": min(45, deadline_days), "status": "green"}
+        ]
+        
+        return {
+            "initiative": initiative_title,
+            "overall_status": "amber",
+            "deadline_date": (datetime.utcnow() + timedelta(days=deadline_days)).isoformat(),
+            "workstreams": tasks
+        }
+        
+    def check_health_and_escalate(self, initiative_data: Dict[str, Any]) -> Dict[str, Any]:
+        """
+        Runs on CRON (`execution.milestone_due`).
+        Escalates if a task belongs in red territory.
+        """
+        red_tasks = [t for t in initiative_data.get("workstreams", []) if t["status"] == "red"]
+        
+        if not red_tasks:
+            return {"status": "healthy", "escalation_needed": False}
+            
+        memo_kwargs = {
+            "decision_context": f"Critical path slippage detected in: {initiative_data['initiative']}",
+            "inputs_used": ["PMO Task Tracker", "Jira Integration"],
+            "assumptions": ["Delaying IT systems merge jeopardizes synergy realization"],
+            "alternatives_considered": ["Extend deadline", "Allocate emergency budget"],
+            "expected_financial_impact": {
+                "revenue_upside_sar": 0.0,
+                "cost_downside_sar": 50000.0,  # Penalty for delay
+                "capital_at_risk_sar": 0.0
+            },
+            "risk_register": [
+                {"risk": "Integration Failure", "severity": "critical", "mitigation": "CEO Intervention required"}
+            ],
+            "required_approvals": ["CEO", "Chief of Staff"],
+            "next_best_action": "Schedule emergency steering committee meeting",
+            "rollback_plan": "N/A - Execution pipeline blocker",
+            "evidence_links": ["https://jira.dealix.local/board/INT-1"]
+        }
+        
+        memo = self.decision_engine.create_memo(
+            agent_id="strategic_pmo",
+            objective="Escalate blocked strategic initiative",
+            recommendation=f"Intervene immediately in the following red tasks: {[t['task'] for t in red_tasks]}",
+            confidence=95.0,
+            **memo_kwargs
+        )
+        
+        # Save escalation to memory
+        self.memory_store.store_item(
+            domain="runbooks",
+            title=f"ESCALATION: {initiative_data['initiative']}",
+            memory_type="Escalation Memo",
+            owner="Chief of Staff",
+            confidence=95,
+            summary=memo.recommendation_ar,
+            tags=["escalation", "red-flag"]
+        )
+        
+        return {"status": "escalated", "memo": memo.to_json()}