diff --git a/lib/crewai/src/crewai/agent/core.py b/lib/crewai/src/crewai/agent/core.py
index db8dcfc65..8f2443e80 100644
--- a/lib/crewai/src/crewai/agent/core.py
+++ b/lib/crewai/src/crewai/agent/core.py
@@ -1912,17 +1912,19 @@ class Agent(BaseAgent):
             )
             raise
 
-    def _execute_and_build_output(
+    def _build_output_from_result(
         self,
+        result: dict[str, Any],
         executor: AgentExecutor,
-        inputs: dict[str, str],
         response_format: type[Any] | None = None,
     ) -> LiteAgentOutput:
-        """Execute the agent and build the output object.
+        """Build a LiteAgentOutput from an executor result dict.
+
+        Shared logic used by both sync and async execution paths.
 
         Args:
+            result: The result dictionary from executor.invoke / invoke_async.
             executor: The executor instance.
-            inputs: Input dictionary for execution.
             response_format: Optional response format.
 
         Returns:
@@ -1930,8 +1932,6 @@ class Agent(BaseAgent):
         """
         import json
 
-        # Execute the agent (this is called from sync path, so invoke returns dict)
-        result = cast(dict[str, Any], executor.invoke(inputs))
         output = result.get("output", "")
 
         # Handle response format conversion
@@ -1979,7 +1979,6 @@ class Agent(BaseAgent):
             else str(raw_output)
         )
 
-        # Extract todo execution results from executor state
         todo_results = LiteAgentOutput.from_todo_items(executor.state.todos.items)
 
         return LiteAgentOutput(
@@ -1994,90 +1993,25 @@ class Agent(BaseAgent):
             last_replan_reason=executor.state.last_replan_reason,
         )
 
+    def _execute_and_build_output(
+        self,
+        executor: AgentExecutor,
+        inputs: dict[str, str],
+        response_format: type[Any] | None = None,
+    ) -> LiteAgentOutput:
+        """Execute the agent synchronously and build the output object."""
+        result = cast(dict[str, Any], executor.invoke(inputs))
+        return self._build_output_from_result(result, executor, response_format)
+
     async def _execute_and_build_output_async(
         self,
         executor: AgentExecutor,
         inputs: dict[str, str],
         response_format: type[Any] | None = None,
     ) -> LiteAgentOutput:
-        """Execute the agent asynchronously and build the output object.
-
-        This is the async version of _execute_and_build_output that uses
-        invoke_async() for native async execution within event loops.
-
-        Args:
-            executor: The executor instance.
-            inputs: Input dictionary for execution.
-            response_format: Optional response format.
-
-        Returns:
-            LiteAgentOutput with raw output, formatted result, and metrics.
-        """
-        import json
-
-        # Execute the agent asynchronously
+        """Execute the agent asynchronously and build the output object."""
         result = await executor.invoke_async(inputs)
-        output = result.get("output", "")
-
-        # Handle response format conversion
-        formatted_result: BaseModel | None = None
-        raw_output: str
-
-        if isinstance(output, BaseModel):
-            formatted_result = output
-            raw_output = output.model_dump_json()
-        elif response_format:
-            raw_output = str(output) if not isinstance(output, str) else output
-            try:
-                model_schema = generate_model_description(response_format)
-                schema = json.dumps(model_schema, indent=2)
-                instructions = self.i18n.slice("formatted_task_instructions").format(
-                    output_format=schema
-                )
-
-                converter = Converter(
-                    llm=self.llm,
-                    text=raw_output,
-                    model=response_format,
-                    instructions=instructions,
-                )
-
-                conversion_result = converter.to_pydantic()
-                if isinstance(conversion_result, BaseModel):
-                    formatted_result = conversion_result
-            except ConverterError:
-                pass  # Keep raw output if conversion fails
-        else:
-            raw_output = str(output) if not isinstance(output, str) else output
-
-        # Get token usage metrics
-        if isinstance(self.llm, BaseLLM):
-            usage_metrics = self.llm.get_token_usage_summary()
-        else:
-            usage_metrics = self._token_process.get_summary()
-
-        raw_str = (
-            raw_output
-            if isinstance(raw_output, str)
-            else raw_output.model_dump_json()
-            if isinstance(raw_output, BaseModel)
-            else str(raw_output)
-        )
-
-        # Extract todo execution results from executor state
-        todo_results = LiteAgentOutput.from_todo_items(executor.state.todos.items)
-
-        return LiteAgentOutput(
-            raw=raw_str,
-            pydantic=formatted_result,
-            agent_role=self.role,
-            usage_metrics=usage_metrics.model_dump() if usage_metrics else None,
-            messages=list(executor.state.messages),
-            plan=executor.state.plan,
-            todos=todo_results,
-            replan_count=executor.state.replan_count,
-            last_replan_reason=executor.state.last_replan_reason,
-        )
+        return self._build_output_from_result(result, executor, response_format)
 
     def _process_kickoff_guardrail(
         self,