diff --git a/app/services/querying.py b/app/services/querying.py
index f94e9ba..de9bd97 100644
--- a/app/services/querying.py
+++ b/app/services/querying.py
@@ -1,3 +1,5 @@
+import json
+import logging
 import os
 from typing import List, Optional
 
@@ -18,6 +20,7 @@ from sqlalchemy.orm import selectinload
 
 from services.compatibility_score import calculate_project_investor_compatibility
 
+logger = logging.getLogger(__name__)
 # Connect to SQLite
 prompt_template = hub.pull("langchain-ai/sql-agent-system-prompt")
 db = SQLDatabase.from_uri(DATABASE_URL)
@@ -58,15 +61,15 @@ class QueryProcessor:
         # Let the LLM handle all database interactions and filtering to get fund IDs
         response = self.agent.invoke(
             {"messages": [("user", question)]},
+            config={"recursion_limit": 50},
         )
 
         # Extract the actual message content
-        ai_response = (
-            response["messages"][-1].content if response.get("messages") else ""
-        )
-
+        logger.info(f"{response}")
+        final_message_content = response["messages"][-1].content
+        logger.info(f"AI Response: \n{final_message_content}")
         # Extract fund IDs from the AI response
-        fund_ids = self._extract_fund_ids_from_response(ai_response)
+        fund_ids = self._extract_fund_ids_from_response(final_message_content)
 
         # Fetch full fund data with investor relationships using the IDs
         return self._fetch_funds_by_ids(fund_ids, project_id)