diff --git a/providers/common/ai/docs/operators/agent.rst b/providers/common/ai/docs/operators/agent.rst
index b3805aa34b765..11e773c75e431 100644
--- a/providers/common/ai/docs/operators/agent.rst
+++ b/providers/common/ai/docs/operators/agent.rst
@@ -156,6 +156,26 @@ tasks can consume it.
     :end-before: [END howto_agent_chain]
 
 
+.. _howto/operator:agent-dynamic-system-prompt:
+
+Dynamic System Prompt
+----------------------
+
+``system_prompt`` is a templated field, so instead of a static string it
+can be a Jinja expression that reads a value an earlier task already
+computed -- for example, tailoring the agent's instructions to a
+classification produced upstream.
+
+.. exampleinclude:: /../../ai/src/airflow/providers/common/ai/example_dags/example_agent.py
+    :language: python
+    :start-after: [START howto_agent_dynamic_system_prompt]
+    :end-before: [END howto_agent_dynamic_system_prompt]
+
+Open the **Rendered Template** tab on the task instance to see the
+substituted ``system_prompt`` after Jinja fills in ``classify``'s XCom
+values.
+
+
 Multi-turn Sessions
 -------------------
 
diff --git a/providers/common/ai/docs/operators/llm.rst b/providers/common/ai/docs/operators/llm.rst
index 1f5a375ca9003..28187d1c183f5 100644
--- a/providers/common/ai/docs/operators/llm.rst
+++ b/providers/common/ai/docs/operators/llm.rst
@@ -186,6 +186,11 @@ to process a list of items in parallel:
     :start-after: [START howto_decorator_llm_pipeline]
     :end-before: [END howto_decorator_llm_pipeline]
 
+.. seealso::
+    :ref:`Dynamic System Prompt <howto/operator:agent-dynamic-system-prompt>` --
+    ``system_prompt`` is templated identically on ``@task.llm``, so the same
+    upstream-XCom pattern applies here.
+
 Human-in-the-Loop Approval
 --------------------------
 
diff --git a/providers/common/ai/src/airflow/providers/common/ai/example_dags/example_agent.py b/providers/common/ai/src/airflow/providers/common/ai/example_dags/example_agent.py
index 787b0d6dce2e1..e294260ca3117 100644
--- a/providers/common/ai/src/airflow/providers/common/ai/example_dags/example_agent.py
+++ b/providers/common/ai/src/airflow/providers/common/ai/example_dags/example_agent.py
@@ -301,3 +301,41 @@ def save_history(session_id: str, transcript: str) -> None:
 # [END howto_agent_session]
 
 example_agent_session()
+
+
+# ---------------------------------------------------------------------------
+# 9. Dynamic system prompt: template system_prompt from an upstream task's XCom
+# ---------------------------------------------------------------------------
+
+
+# [START howto_agent_dynamic_system_prompt]
+@dag(tags=["example"])
+def example_agent_dynamic_system_prompt():
+    @task
+    def classify(ticket: str) -> dict:
+        category = "shipping" if "order" in ticket.lower() else "other"
+        return {"priority": "high", "category": category}
+
+    @task.agent(
+        llm_conn_id="pydanticai_default",
+        # system_prompt is a templated field -- Jinja renders it at task-run
+        # time, pulling the classification an upstream task already computed.
+        system_prompt=(
+            "You are handling a {{ ti.xcom_pull(task_ids='classify')['priority'] }}-priority "
+            "'{{ ti.xcom_pull(task_ids='classify')['category'] }}' ticket. "
+            "Draft a concise, friendly reply."
+        ),
+    )
+    def draft_reply(ticket: str, triage: dict) -> str:
+        # `triage` creates the task dependency; its content also flows into
+        # system_prompt via Jinja above. The returned string is the *prompt*
+        # sent to the agent -- the drafted reply is this task's XCom output.
+        return f"Draft a reply for: {ticket}"
+
+    ticket = "Where is my order? It still hasn't shipped."
+    draft_reply(ticket, classify(ticket))
+
+
+# [END howto_agent_dynamic_system_prompt]
+
+example_agent_dynamic_system_prompt()