chore: import upstream snapshot with attribution

contributing/samples/patterns/workflow_triage/execution_agent.py

@@ -0,0 +1,116 @@
+# Copyright 2026 Google LLC
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+
+from typing import Optional
+
+from google.adk.agents import Agent
+from google.adk.agents import ParallelAgent
+from google.adk.agents.base_agent import BeforeAgentCallback
+from google.adk.agents.callback_context import CallbackContext
+from google.adk.agents.readonly_context import ReadonlyContext
+from google.adk.agents.sequential_agent import SequentialAgent
+from google.genai import types
+
+
+def before_agent_callback_check_relevance(
+    agent_name: str,
+) -> BeforeAgentCallback:
+  """Callback to check if the state is relevant before executing the agent."""
+
+  def callback(callback_context: CallbackContext) -> Optional[types.Content]:
+    """Check if the state is relevant."""
+    if agent_name not in callback_context.state["execution_agents"]:
+      return types.Content(
+          parts=[
+              types.Part(
+                  text=(
+                      f"Skipping execution agent {agent_name} as it is not"
+                      " relevant to the current state."
+                  )
+              )
+          ]
+      )
+
+  return callback
+
+
+code_agent = Agent(
+    name="code_agent",
+    instruction="""\
+You are the Code Agent, responsible for generating code.
+
+NOTE: You should only generate code and ignore other askings from the user.
+""",
+    before_agent_callback=before_agent_callback_check_relevance("code_agent"),
+    output_key="code_agent_output",
+)
+
+math_agent = Agent(
+    name="math_agent",
+    instruction="""\
+You are the Math Agent, responsible for performing mathematical calculations.
+
+NOTE: You should only perform mathematical calculations and ignore other askings from the user.
+""",
+    before_agent_callback=before_agent_callback_check_relevance("math_agent"),
+    output_key="math_agent_output",
+)
+
+
+worker_parallel_agent = ParallelAgent(
+    name="worker_parallel_agent",
+    sub_agents=[
+        code_agent,
+        math_agent,
+    ],
+)
+
+
+def instruction_provider_for_execution_summary_agent(
+    readonly_context: ReadonlyContext,
+) -> str:
+  """Provides the instruction for the execution agent."""
+  activated_agents = readonly_context.state["execution_agents"]
+  prompt = f"""\
+You are the Execution Summary Agent, responsible for summarizing the execution of the plan in the current invocation.
+
+In this invocation, the following agents were involved: {', '.join(activated_agents)}.
+
+Below are their outputs:
+"""
+  for agent_name in activated_agents:
+    output = readonly_context.state.get(f"{agent_name}_output", "")
+    prompt += f"\n\n{agent_name} output:\n{output}"
+
+  prompt += (
+      "\n\nPlease summarize the execution of the plan based on the above"
+      " outputs."
+  )
+  return prompt.strip()
+
+
+execution_summary_agent = Agent(
+    name="execution_summary_agent",
+    instruction=instruction_provider_for_execution_summary_agent,
+    include_contents="none",
+)
+
+plan_execution_agent = SequentialAgent(
+    name="plan_execution_agent",
+    sub_agents=[
+        worker_parallel_agent,
+        execution_summary_agent,
+    ],
+)