Create agents.py

agents.py

@@ -0,0 +1,47 @@
+# agents.py
+import config
+
+class LogisticsAgent:
+    """
+    Base class for specific agents. In a real LLM app, 
+    these would call OpenAI/Anthropic APIs.
+    """
+    def __init__(self, name, role):
+        self.name = name
+        self.role = role
+
+    def negotiate(self):
+        raise NotImplementedError
+
+class CostAgent(LogisticsAgent):
+    def negotiate(self):
+        return {
+            "msg": f"💰 {self.name}: 'We must cut fuel costs. Capping distance to {config.LIMIT_COST_STRATEGY}.'",
+            "constraint": config.LIMIT_COST_STRATEGY,
+            "label": "Budget-Focused"
+        }
+
+class EcoAgent(LogisticsAgent):
+    def negotiate(self):
+        return {
+            "msg": f"🌿 {self.name}: 'Sustainability priority active. Strict range limit of {config.LIMIT_ECO_STRATEGY}.'",
+            "constraint": config.LIMIT_ECO_STRATEGY,
+            "label": "Eco-Friendly"
+        }
+
+class DispatcherAgent(LogisticsAgent):
+    def negotiate(self):
+        return {
+            "msg": f"⚡ {self.name}: 'Delivery speed is paramount. Removing range limits.'",
+            "constraint": config.LIMIT_SPEED_STRATEGY,
+            "label": "High Velocity"
+        }
+
+def get_agent_decision(strategy_mode):
+    """Factory function to return the correct agent's decision."""
+    if strategy_mode == "Cost-Killer":
+        return CostAgent("Accountant", "Budget").negotiate()
+    elif strategy_mode == "Eco-Mode":
+        return EcoAgent("Greta", "Environment").negotiate()
+    else:
+        return DispatcherAgent("OpsLead", "Speed").negotiate()