refactor: streamline tool_choice reset logic

This update moves the tool_choice reset logic to a more appropriate location within the RunImpl class, ensuring that the original agent's model_settings remains unmodified during the reset process. The logic now checks for problematic scenarios before creating a modified copy of the agent's settings, maintaining expected behavior across sequential runs. This change enhances clarity and efficiency in handling tool choices.

Addresses previous feedback regarding the modification of the agent instance and improves the overall structure of the reset logic.

Author: mini-peanut

src/agents/_run_impl.py

@@ -211,15 +211,6 @@ async def execute_tools_and_side_effects(
         # Reset tool_choice to "auto" after tool execution to prevent infinite loops
         if processed_response.functions or processed_response.computer_actions:
             tools = agent.tools
-            # Only reset in the problematic scenarios where loops are likely unintentional
-            if cls._should_reset_tool_choice(agent.model_settings, tools):
-                # Create a modified copy instead of modifying the original agent
-                new_model_settings = dataclasses.replace(
-                    agent.model_settings,
-                    tool_choice="auto"
-                )
-                # Create a new internal agent with updated settings
-                agent = dataclasses.replace(agent, model_settings=new_model_settings)
 
             if (
                 run_config.model_settings and
@@ -233,6 +224,16 @@ async def execute_tools_and_side_effects(
                 # Create a new run_config with the new settings
                 run_config = dataclasses.replace(run_config, model_settings=new_run_config_settings)
 
+            # Only reset in the problematic scenarios where loops are likely unintentional
+            if cls._should_reset_tool_choice(agent.model_settings, tools):
+                # Create a modified copy instead of modifying the original agent
+                new_model_settings = dataclasses.replace(
+                    agent.model_settings,
+                    tool_choice="auto"
+                )
+                # Create a new internal agent with updated settings
+                agent = dataclasses.replace(agent, model_settings=new_model_settings)
+
         # Second, check if there are any handoffs
         if run_handoffs := processed_response.handoffs:
             return await cls.execute_handoffs(