Upload settings.py

config/settings.py

@@ -167,11 +167,15 @@ class Settings:
             "advisor": self.models.agent3_model,
             "generator": self.models.agent4_model,
             
-            # Stage 2 New Architecture agents
-            "brand_identifier": self.models.agent3_model,  # Llama 70B - needs context understanding
-            "benchmark_advisor": self.models.agent3_model,  # Llama 70B - needs reasoning
-            "best_practices_validator": self.models.agent3_model,  # Llama 70B - needs judgment
-            "head_synthesizer": self.models.agent3_model,  # Llama 70B - needs synthesis
+            # Stage 2 New Architecture agents — optimized model per role
+            # AURORA: Creative/visual reasoning for brand color analysis
+            "brand_identifier": os.getenv("BRAND_IDENTIFIER_MODEL", "Qwen/Qwen2.5-72B-Instruct"),
+            # ATLAS: 128K context for large benchmark data, strong comparative reasoning
+            "benchmark_advisor": os.getenv("BENCHMARK_ADVISOR_MODEL", "meta-llama/Llama-3.3-70B-Instruct"),
+            # SENTINEL: Methodical rule-following, precise judgment
+            "best_practices_validator": os.getenv("BEST_PRACTICES_MODEL", "Qwen/Qwen2.5-72B-Instruct"),
+            # NEXUS: 128K context for combined inputs, strong synthesis
+            "head_synthesizer": os.getenv("HEAD_SYNTHESIZER_MODEL", "meta-llama/Llama-3.3-70B-Instruct"),
             "benchmark_extractor": self.models.agent2_model,  # Phi-3.5 - structured extraction
         }
         return model_map.get(agent_name, self.models.fallback_model)