google--adk-python/contributing/samples/workflows/agent_in_workflow

Agents In Workflow

This sample demonstrates how to use both task mode and single_turn mode Agents as nodes within a Workflow.

Overview

The workflow represents a medical lab intake process:

1. intake_agent: A task mode Agent that chats with the user to collect their name and phone_number. It handles a multi-turn conversation until its PatientIdentity output schema is fulfilled.
2. check_identity: A regular Python function node that receives the PatientIdentity. It mocks checking the database.
   • If the name is anything other than "Jane Doe", it yields a retry route, sending the user back to the intake_agent.
   • If the name is "Jane Doe", it routes to the generate_instruction agent.
3. generate_instruction: A single_turn mode Agent that uses the find_orders tool to look up orders. It requires tool confirmation before execution.

Sample Inputs

• Hi, I am Jane Doe, my phone number is 555-1234.

  The system will process this and return the mock lab orders along with AI-generated instructions on how to prepare.

• I'm here for my blood work.

  The system will ask for your name and phone number.

• My name is John Doe, and my number is 123-456-7890.

  The system will fail to find John's orders and route back to the intake agent.

Graph

                  [ START ]
                      |
                      v
              [ intake_agent ] <----.
                      |             |
                      v             |
               [ check_identity ] --- retry
                      |
                      | (DEFAULT_ROUTE)
                      v
          [ generate_instruction ]

How To

Within an ADK workflow, you can embed LLM agents directly as nodes. The ADK runner handles them according to their mode:

1. Task Mode Agents

A task agent (mode="task") handles a multi-turn conversation on its own before passing control to the next node. It will continually interact with the user until its specified task is completed.

class PatientIdentity(BaseModel):
  name: str
  phone_number: str

intake_agent = Agent(
    name="intake_agent",
    mode="task", # Stops and chats with the user until the schema is populated
    output_schema=PatientIdentity,
    instruction="...",
)

The parsed output_schema object is automatically forwarded as the node_input to the next node in the graph.

2. Single Turn Mode Agents

A single_turn agent (the default mode if omitted) executes a single LLM call. It is typically used for inline text generation, summarization, or classification without chatting with the user.

generate_instruction = Agent(
    name="generate_instruction",
    tools=[FunctionTool(find_orders, require_confirmation=True)],
    instruction="""
Use the find_orders tool to get the patient's orders.
List the orders found, and then generate a concise instruction about how to prepare based on those orders.
""",
)

In this sample, the generate_instruction agent uses the find_orders tool to retrieve the orders. It also demonstrates tool confirmation, requiring the user to approve the tool call before it executes.