Integrate lead_scoring_agent: score leads at start, inject scoring fields into asset flow

recovery_enrichment_agent.py

@@ -1,13 +1,13 @@
 """
 Recovery/Enrichment Agent
-
 Implements prioritized enrichment source config, rate limiting, retry/backoff, queue/handoff, and full action/decision logging for leads enrichment and recovery workflows.
+Integrates lead_scoring_agent to score leads at the start of processing and inject scoring fields into the asset flow for analytics and UI visibility.
 """
-
 import time
 import logging
 from enum import Enum
 from typing import List, Dict, Any, Optional
+from lead_scoring_agent import score_lead
 
 # Agent config
 class EnrichmentSource(Enum):
@@ -34,22 +34,32 @@ class LeadBatchHandler:
         self.history = []
         self.rate_limit_counter = 0
         self.last_reset = time.time()
-
+    
     def reset_rate_limit(self):
         now = time.time()
         if now - self.last_reset > 60:
             self.rate_limit_counter = 0
             self.last_reset = now
             log_event("rate_limit_reset", {})
-
+    
     def handle_lead_batch(self, leads: List[Dict[str, Any]]):
         results = []
         for lead in leads:
             result = self._process_lead(lead)
             results.append(result)
         return results
-
+    
     def _process_lead(self, lead: Dict[str, Any]) -> Dict[str, Any]:
+        # Score the lead at the start of processing and inject scoring fields
+        scoring_data = score_lead(lead)
+        lead.update({
+            "lead_score": scoring_data.get("score", 0),
+            "score_tier": scoring_data.get("tier", "unknown"),
+            "score_factors": scoring_data.get("factors", {}),
+            "scored_at": time.time()
+        })
+        log_event("lead_scored", {"lead": lead, "scoring": scoring_data})
+        
         self.reset_rate_limit()
         for source in self.config["priority_sources"]:
             for attempt in range(self.config["max_retries"]):
@@ -64,13 +74,14 @@ class LeadBatchHandler:
                 success, data = self._enrich_from_source(source, lead)
                 if success:
                     log_event("enrich_success", {"source": source.value, "lead": lead})
-                    return {"lead": lead, "enriched": data}
+                    # Preserve scoring fields in enriched result
+                    return {"lead": lead, "enriched": data, "lead_score": lead["lead_score"], "score_tier": lead["score_tier"]}
                 else:
                     log_event("enrich_failure", {"source": source.value, "lead": lead, "attempt": attempt+1})
                     time.sleep(self.config["retry_backoff_seconds"])
         self._escalate_to_human(lead)
-        return {"lead": lead, "enriched": None, "escalated": True}
-
+        return {"lead": lead, "enriched": None, "escalated": True, "lead_score": lead["lead_score"], "score_tier": lead["score_tier"]}
+    
     def _enrich_from_source(self, source: EnrichmentSource, lead: Dict[str, Any]) -> (bool, Optional[dict]):
         # Simulation logic; replace with real API/browser/human integration
         if source == EnrichmentSource.API:
@@ -80,11 +91,11 @@ class LeadBatchHandler:
         if source == EnrichmentSource.HUMAN_REVIEW:
             return True, {"company": "Simulated Corp", "status": "Human reviewed"}
         return False, None
-
+    
     def _escalate_to_human(self, lead: Dict[str, Any]):
         self.queue.append(lead)
         log_event("escalate", {"lead": lead})
-
+    
     def get_audit_log(self):
         # Return all logged history (simulate)
         pass
@@ -102,4 +113,4 @@ def simulate_lead_handoff():
     # add more logic for audit/fallback/escalation as needed
 
 if __name__ == "__main__":
-    simulate_lead_handoff()
+    simulate_lead_handoff()