diff --git a/python/packages/core/agent_framework/_tools.py b/python/packages/core/agent_framework/_tools.py
index ce9606d3de..688b4c5381 100644
--- a/python/packages/core/agent_framework/_tools.py
+++ b/python/packages/core/agent_framework/_tools.py
@@ -319,6 +319,7 @@ def __init__(
         func: Callable[..., Any] | None = None,
         input_model: type[BaseModel] | Mapping[str, Any] | None = None,
         result_parser: Callable[[Any], str | list[Content]] | _SkipParsingSentinel | None = None,
+        concurrency_group: str | None = None,
         **kwargs: Any,
     ) -> None:
         """Initialize the FunctionTool.
@@ -333,10 +334,11 @@ def __init__(
             max_invocations: The maximum number of times this function can be invoked
                 across the **lifetime of this tool instance**. If None (default),
                 there is no limit. Should be at least 1. If the tool is called multiple
-                times in one iteration, those will execute, after that it will stop working. For example,
-                if max_invocations is 3 and the tool is called 5 times in a single iteration,
-                these will complete, but any subsequent calls to the tool (in the same or future iterations)
-                will raise a ToolException.
+                times in one iteration, those will execute, after that it will stop
+                working. For example, if max_invocations is 3 and the tool is called 5
+                times in a single iteration, these will complete, but any subsequent
+                calls to the tool (in the same or future iterations) will raise a
+                ToolException.
 
                 .. note::
                     This counter lives on the tool instance and is never automatically
@@ -347,30 +349,37 @@ def __init__(
                     ``FunctionInvocationConfiguration["max_function_calls"]``
                     for per-request limits instead.
 
-            max_invocation_exceptions: The maximum number of exceptions allowed during invocations.
-                If None, there is no limit. Should be at least 1.
+            max_invocation_exceptions: The maximum number of exceptions allowed
+                during invocations. If None, there is no limit. Should be at least 1.
             additional_properties: Additional properties to set on the function.
-            func: The function to wrap. When ``None``, creates a declaration-only tool
-                that has no implementation. Declaration-only tools are useful when you want
-                the agent to reason about tool usage without executing them, or when the
-                actual implementation exists elsewhere (e.g., client-side rendering).
-            input_model: The Pydantic model that defines the input parameters for the function.
-                This can also be a JSON schema dictionary.
-                If not provided and ``func`` is not ``None``, it will be inferred from
-                the function signature. When ``func`` is ``None`` and ``input_model`` is
-                not provided, the tool will use an empty input model (no parameters) in
-                its JSON schema. For declaration-only tools that should declare
-                parameters, explicitly provide ``input_model`` (either a Pydantic
-                ``BaseModel`` or a JSON schema dictionary) so the model can reason about
-                the expected arguments.
-            result_parser: An optional callable with signature ``Callable[[Any], str]`` that
-                overrides the default result parsing behavior. When provided, this callable
-                is used to convert the raw function return value to a string instead of the
-                built-in :meth:`parse_result` logic. Pass the :data:`SKIP_PARSING` sentinel
-                instead of a callable to opt out of parsing entirely; in that case
-                :meth:`invoke` returns the wrapped function's raw return value. Depending
-                on your function, it may be easiest to just do the serialization directly
-                in the function body rather than providing a custom ``result_parser``.
+            func: The function to wrap. When ``None``, creates a declaration-only
+                tool that has no implementation. Declaration-only tools are useful
+                when you want the agent to reason about tool usage without executing
+                them, or when the actual implementation exists elsewhere (e.g.,
+                client-side rendering).
+            input_model: The Pydantic model that defines the input parameters for the
+                function. This can also be a JSON schema dictionary.
+                If not provided and ``func`` is not ``None``, it will be inferred
+                from the function signature. When ``func`` is ``None`` and
+                ``input_model`` is not provided, the tool will use an empty input
+                model (no parameters) in its JSON schema. For declaration-only tools
+                that should declare parameters, explicitly provide ``input_model``
+                (either a Pydantic ``BaseModel`` or a JSON schema dictionary) so the
+                model can reason about the expected arguments.
+            result_parser: An optional callable with signature ``Callable[[Any], str]``
+                that overrides the default result parsing behavior. When provided,
+                this callable is used to convert the raw function return value to a
+                string instead of the built-in :meth:`parse_result` logic. Pass the
+                :data:`SKIP_PARSING` sentinel instead of a callable to opt out of
+                parsing entirely; in that case :meth:`invoke` returns the wrapped
+                function's raw return value. Depending on your function, it may be
+                easiest to just do the serialization directly in the function body
+                rather than providing a custom ``result_parser``.
+            concurrency_group: If provided, tool calls with the same
+                concurrency_group will execute sequentially in the order they were
+                invoked by the model. Tools without a group, or with different
+                groups, will execute concurrently. Useful for stateful tools with
+                write->read dependencies to prevent race conditions.
             **kwargs: Additional keyword arguments.
         """
         # Core attributes (formerly from BaseTool)
@@ -415,6 +424,7 @@ def __init__(
         self._invocation_duration_histogram = _default_histogram()
         self.type: Literal["function_tool"] = "function_tool"
         self.result_parser = result_parser
+        self.concurrency_group = concurrency_group
 
     def _discover_injected_parameters(self) -> None:
         """Inspect the wrapped function for runtime injection parameters."""
@@ -898,9 +908,13 @@ def to_json_schema_spec(self) -> dict[str, Any]:
     @override
     def to_dict(self, *, exclude: set[str] | None = None, exclude_none: bool = True) -> dict[str, Any]:
         as_dict = super().to_dict(exclude=exclude, exclude_none=exclude_none)
+        if (not exclude or "concurrency_group" not in exclude) and (
+            not exclude_none or self.concurrency_group is not None
+        ):
+            as_dict["concurrency_group"] = self.concurrency_group
         if (exclude and "input_model" in exclude) or not self.input_model:
             return as_dict
-        as_dict["input_model"] = self.parameters()  # Use cached parameters()
+        as_dict["input_model"] = self.parameters()
         return as_dict
 
 
@@ -1137,6 +1151,7 @@ def tool(
     max_invocations: int | None = None,
     max_invocation_exceptions: int | None = None,
     additional_properties: dict[str, Any] | None = None,
+    concurrency_group: str | None = None,
     result_parser: Callable[[Any], str | list[Content]] | _SkipParsingSentinel | None = None,
 ) -> FunctionTool: ...
 
@@ -1153,6 +1168,7 @@ def tool(
     max_invocations: int | None = None,
     max_invocation_exceptions: int | None = None,
     additional_properties: dict[str, Any] | None = None,
+    concurrency_group: str | None = None,
     result_parser: Callable[[Any], str | list[Content]] | _SkipParsingSentinel | None = None,
 ) -> Callable[[Callable[..., Any]], FunctionTool]: ...
 
@@ -1168,6 +1184,7 @@ def tool(
     max_invocations: int | None = None,
     max_invocation_exceptions: int | None = None,
     additional_properties: dict[str, Any] | None = None,
+    concurrency_group: str | None = None,
     result_parser: Callable[[Any], str | list[Content]] | _SkipParsingSentinel | None = None,
 ) -> FunctionTool | Callable[[Callable[..., Any]], FunctionTool]:
     """Decorate a function to turn it into a FunctionTool that can be passed to models and executed automatically.
@@ -1212,6 +1229,11 @@ def tool(
         max_invocation_exceptions: The maximum number of exceptions allowed during invocations.
             If None, there is no limit, should be at least 1.
         additional_properties: Additional properties to set on the function.
+        concurrency_group: If provided, tool calls with the same
+            concurrency_group will execute sequentially in the order they were
+            invoked by the model. Tools without a group, or with different
+            groups, will execute concurrently. Useful for stateful tools with
+            write->read dependencies to prevent race conditions.
         result_parser: An optional callable with signature ``Callable[[Any], str]`` that
             overrides the default result parsing. When provided, this callable converts the
             raw function return value to a string instead of using the built-in
@@ -1312,6 +1334,7 @@ def wrapper(f: Callable[..., Any]) -> FunctionTool:
                 func=f,
                 input_model=schema,
                 result_parser=result_parser,
+                concurrency_group=concurrency_group,
             )
 
         return wrapper(func)
@@ -1377,6 +1400,7 @@ class FunctionInvocationConfiguration(TypedDict, total=False):
     terminate_on_unknown_calls: bool
     additional_tools: Sequence[FunctionTool]
     include_detailed_errors: bool
+    tool_execution_order: Literal["parallel", "sequential"]
 
 
 def normalize_function_invocation_configuration(
@@ -1390,6 +1414,7 @@ def normalize_function_invocation_configuration(
         "terminate_on_unknown_calls": False,
         "additional_tools": [],
         "include_detailed_errors": False,
+        "tool_execution_order": "parallel",
     }
     if config:
         normalized.update(config)
@@ -1820,25 +1845,54 @@ async def _try_execute_function_call_groups(
     # Only a fully executable batch reaches this point; run calls concurrently but retain per-call result groups.
     # Create each task inside a copied context so the active agent span is
     # preserved for every parallel tool invocation.
-    execution_tasks = [
-        contextvars.copy_context().run(
-            asyncio.create_task,
-            _execute_single_function_call(
-                function_call,
-                custom_args=custom_args,
-                config=config,
-                tool_map=tool_map,
-                invocation_session=invocation_session,
-                middleware_pipeline=middleware_pipeline,
-                live_tools=live_tools,
-            ),
-        )
-        for function_call in function_calls
-    ]
-    execution_results = await asyncio.gather(*execution_tasks)
+    execution_order = config.get("tool_execution_order", "parallel")
+
+    groups: dict[str, list[int]] = {}
+    for idx, function_call in enumerate(function_calls):
+        group_key: str | None = None
+
+        if execution_order == "parallel":
+            tool = tool_map.get(function_call.name)
+            if tool is not None:
+                group_key = getattr(tool, "concurrency_group", None)
+
+        if group_key is None:
+            group_key = "__sequential_all__" if execution_order == "sequential" else f"__ungrouped_{idx}"
+
+        if group_key not in groups:
+            groups[group_key] = []
+        groups[group_key].append(idx)
+
+    ordered_results: list[tuple[list[Content], bool] | None] = [None] * len(function_calls)
+
+    async def _execute_group(indices: list[int]) -> None:
+        for idx in indices:
+            call = function_calls[idx]
+            ctx = contextvars.copy_context()
+            task = ctx.run(
+                asyncio.create_task,
+                _execute_single_function_call(
+                    call,
+                    custom_args=custom_args,
+                    config=config,
+                    tool_map=tool_map,
+                    invocation_session=invocation_session,
+                    middleware_pipeline=middleware_pipeline,
+                    live_tools=live_tools,
+                ),
+            )
+            res = await task
+            ordered_results[idx] = res
 
-    should_terminate = any(terminate for _, terminate in execution_results)
-    return [result_contents for result_contents, _ in execution_results], should_terminate
+    execution_tasks = [asyncio.create_task(_execute_group(indices)) for indices in groups.values()]
+
+    await asyncio.gather(*execution_tasks)
+
+    if any(result is None for result in ordered_results):
+        raise RuntimeError("Internal error: missing tool execution result(s).")
+    completed_results = cast(list[tuple[list[Content], bool]], ordered_results)
+    should_terminate = any(terminate for _, terminate in completed_results)
+    return [result_contents for result_contents, _ in completed_results], should_terminate
 
 
 @dataclass
@@ -3259,11 +3313,19 @@ def get_response(
         raw_session = request_kwargs.get("session")
         invocation_session = raw_session if isinstance(raw_session, _AgentSession) else None
 
+        # Give the loop private mutable options and one shared run-local tool list for progressive tool changes.
+        # Make options mutable so we can update conversation_id during function invocation loop
+        mutable_options: dict[str, Any] = dict(options) if options else {}
+
         # Bind one executor with the run's custom arguments, middleware, configuration, and session.
+        request_config = dict(self.function_invocation_configuration)
+        if tool_exec_order := mutable_options.pop("tool_execution_order", None):
+            request_config["tool_execution_order"] = tool_exec_order
+
         execute_function_calls = partial(
             _execute_function_calls,
             custom_args=additional_function_arguments,
-            config=self.function_invocation_configuration,
+            config=request_config,
             invocation_session=invocation_session,
             middleware_pipeline=function_middleware_pipeline,
         )
diff --git a/python/packages/core/agent_framework/_types.py b/python/packages/core/agent_framework/_types.py
index 7a0a982ba8..a9fa592af3 100644
--- a/python/packages/core/agent_framework/_types.py
+++ b/python/packages/core/agent_framework/_types.py
@@ -3552,6 +3552,10 @@ class _ChatOptionsBase(TypedDict, total=False):
     tool_choice: ToolMode | Literal["auto", "required", "none"]
     allow_multiple_tool_calls: bool
 
+    # Dictates whether multiple tool calls in a single message batch
+    # are executed concurrently (parallel) or one-by-one (sequential).
+    tool_execution_order: Literal["parallel", "sequential"]
+
     # Response configuration
     response_format: type[BaseModel] | Mapping[str, Any] | None
 
diff --git a/python/packages/core/tests/core/test_tools.py b/python/packages/core/tests/core/test_tools.py
index a24e20cacd..bd45d7ee18 100644
--- a/python/packages/core/tests/core/test_tools.py
+++ b/python/packages/core/tests/core/test_tools.py
@@ -20,6 +20,7 @@
     _auto_invoke_function,
     _parse_annotation,
     _parse_inputs,
+    _try_execute_function_call_groups,
     normalize_function_invocation_configuration,
 )
 from agent_framework.observability import OtelAttr
@@ -1547,3 +1548,106 @@ def test_skip_parsing_is_singleton() -> None:
 
 
 # endregion
+
+
+def test_tool_decorator_accepts_concurrency_group():
+    """Test that the @tool decorator accepts and stores the concurrency_group parameter."""
+
+    @tool(name="grouped_tool", concurrency_group="file_system")
+    def grouped_tool(x: int) -> int:
+        return x
+
+    assert isinstance(grouped_tool, FunctionTool)
+    assert grouped_tool.concurrency_group == "file_system"
+
+
+def test_function_invocation_configuration_accepts_execution_order():
+    """Test that execution_order is accepted and defaults to 'parallel'."""
+    config_seq = normalize_function_invocation_configuration({"tool_execution_order": "sequential"})
+    assert config_seq["tool_execution_order"] == "sequential"
+
+    config_default = normalize_function_invocation_configuration(None)
+    assert config_default["tool_execution_order"] == "parallel"
+
+
+async def test_try_execute_function_call_groups_concurrency_group():
+    """Tools in the same concurrency_group execute sequentially; ungrouped tools run concurrently."""
+    execution_order = []
+
+    @tool(concurrency_group="files")
+    async def write_file(name: str):
+        execution_order.append("write_start")
+        await asyncio.sleep(0.05)
+        execution_order.append("write_end")
+        return f"wrote {name}"
+
+    @tool(concurrency_group="files")
+    async def read_file(name: str):
+        execution_order.append("read_start")
+        await asyncio.sleep(0.01)
+        execution_order.append("read_end")
+        return f"read {name}"
+
+    @tool()
+    async def ungrouped_tool():
+        execution_order.append("ungrouped_start")
+        await asyncio.sleep(0.02)
+        execution_order.append("ungrouped_end")
+        return "ungrouped"
+
+    # Create function call contents simulating a batch from the LLM
+    call_write = Content.from_function_call(call_id="1", name="write_file", arguments='{"name": "test"}')
+    call_read = Content.from_function_call(call_id="2", name="read_file", arguments='{"name": "test"}')
+    call_ungrouped = Content.from_function_call(call_id="3", name="ungrouped_tool", arguments="{}")
+
+    config = normalize_function_invocation_configuration(None)
+
+    results, should_terminate = await _try_execute_function_call_groups(
+        custom_args={},
+        function_calls=[call_write, call_read, call_ungrouped],
+        tools=[write_file, read_file, ungrouped_tool],
+        config=config,
+    )
+
+    assert not should_terminate
+    assert len(results) == 3
+
+    assert execution_order.index("write_end") < execution_order.index("read_start")
+    assert execution_order.index("ungrouped_start") < execution_order.index("write_end")
+
+
+async def test_try_execute_function_call_groups_sequential_config():
+    """When execution_order is 'sequential', ALL tools run one-by-one regardless of groups."""
+    execution_order = []
+
+    @tool()
+    async def tool_a():
+        execution_order.append("a_start")
+        await asyncio.sleep(0.03)
+        execution_order.append("a_end")
+        return "a"
+
+    @tool()
+    async def tool_b():
+        execution_order.append("b_start")
+        await asyncio.sleep(0.01)
+        execution_order.append("b_end")
+        return "b"
+
+    call_a = Content.from_function_call(call_id="1", name="tool_a", arguments="{}")
+    call_b = Content.from_function_call(call_id="2", name="tool_b", arguments="{}")
+
+    config = normalize_function_invocation_configuration({"tool_execution_order": "sequential"})
+
+    results, should_terminate = await _try_execute_function_call_groups(
+        custom_args={},
+        function_calls=[call_a, call_b],
+        tools=[tool_a, tool_b],
+        config=config,
+    )
+
+    assert not should_terminate
+    assert execution_order == ["a_start", "a_end", "b_start", "b_end"]
+
+
+# endregion