docs: Fix links (#5111)

Fix links

Signed-off-by: Sherlock113 <sherlockxu07@gmail.com>

docs/source/build-with-bentoml/clients.rst

@@ -138,7 +138,7 @@ Alternatively, use the ``server_ready_timeout`` parameter to specify the maximum
 Call a task endpoint
 --------------------
 
-You can create clients to interact with Services defined with :ref:`task <bentoml-tasks>` endpoints by submitting inputs and then asynchronously checking for results at a later time. This is particularly useful for scenarios where the client does not need to actively wait for the task to complete. For more information, see :doc:`/guides/tasks`.
+You can create clients to interact with Services defined with :ref:`task <bentoml-tasks>` endpoints by submitting inputs and then asynchronously checking for results at a later time. This is particularly useful for scenarios where the client does not need to actively wait for the task to complete. For more information, see :doc:`/get-started/async-task-queues`.
 
 Input and output
 ----------------

docs/source/build-with-bentoml/services.rst

@@ -266,7 +266,7 @@ Tasks
 
 Tasks in BentoML allow you to execute long-running operations in the background, managed via a task queue style API. These background tasks are ideal for scenarios like batch processing and image or video generation where you don't need the results immediately or synchronously.
 
-To define a task endpoint, use the ``@bentoml.task`` decorator in the Service constructor. For more information, see :doc:`/guides/tasks`.
+To define a task endpoint, use the ``@bentoml.task`` decorator in the Service constructor. For more information, see :doc:`/get-started/async-task-queues`.
 
 Convert legacy Runners to a Service
 -----------------------------------

docs/source/examples/langgraph.rst

@@ -132,7 +132,7 @@ The ``service.py`` file defines the ``SearchAgentService``, a BentoML Service th
 
    After that, define the LangGraph workflow that uses the model. The LangGraph agent will call this model and build its flow with nodes and edges, connecting the outputs of the LLM with the rest of the system. For detailed explanations of implementing LangGraph workflows, see `the LangGraph documentation <https://langchain-ai.github.io/langgraph/#example>`_.
 
-3. Define a BentoML :doc:`task </guides/tasks>` endpoint ``invoke`` with ``@bentoml.task`` to handle the LangGraph workflow asynchronously. It is a background task that supports long-running operations. This ensures that complex LangGraph workflows involving external tools can complete without timing out.
+3. Define a BentoML :doc:`task </get-started/async-task-queues>` endpoint ``invoke`` with ``@bentoml.task`` to handle the LangGraph workflow asynchronously. It is a background task that supports long-running operations. This ensures that complex LangGraph workflows involving external tools can complete without timing out.
 
    After sending the user's query to the LangGraph agent, the task retrieves the final state and provides the results back to the user.