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Module 21.6: Graph-based Workflows - Deterministic Routing

Theory​

In Module 21.5, you learned about Dynamic Workflows using the @node decorator. These allow you to use standard Python code to decide which node to run next.

However, there are many scenarios where the flow of your application is predictable and structured. For these cases, ADK 2.0 provides the Workflow class, which allows you to define a graph using explicit Edges.

Why use Deterministic Workflows?​

  1. Transparency: The structure of your application is visible at a glance in the code (and in the Dev UI Graph View).
  2. Efficiency: Routing via a dictionary or a fixed list of edges is faster and cheaper than asking an LLM to decide the next step.
  3. Reliability: You eliminate the "hallucination" risk of an LLM choosing a non-existent path.

Anatomy of a Workflow Graph​

A Workflow in ADK 2.0 is defined by two things:

  1. Nodes: The individual components (Agents, FunctionTools, or @node functions).
  2. Edges: The connections between nodes, defining the execution path.

Static Edges (Sequential)​

The simplest edge is a tuple ("NodeA", "NodeB"), which means "after NodeA finishes, run NodeB".

from google.adk import Workflow

# A simple sequential pipeline: START -> Researcher -> Writer
my_workflow = Workflow(
    name="SequentialSystem",
    edges=[
        ("START", researcher_node),
        (researcher_node, writer_node)
    ]
)

Router Edges (Conditional)​

To build a router without writing a full @node function, you can use a dictionary as the target of an edge. The key in the dictionary corresponds to the output of the previous node.

from google.adk import Workflow

# A system that routes based on a classifier's output
my_workflow = Workflow(
    name="SmartRouter",
    edges=[
        ("START", classifier_node),
        (classifier_node, {
            "technical": tech_agent,
            "billing": billing_agent,
            "other": general_agent
        })
    ]
)

The "START" and "END" Reserved Keywords​

  • START: The entry point of your workflow. Every workflow must have at least one edge originating from "START".
  • END: (Optional) Explicitly signals that the workflow has finished. If a node has no outgoing edges, the workflow ends automatically.

Data Flow in Deterministic Workflows​

In a deterministic Workflow, the output of one node is automatically passed as the input to the next node.

  • If NodeA returns a string, NodeB receives that string as its input.
  • If you use a Router Dictionary, the output of the previous node must match one of the keys in the dictionary.

Key Takeaways​

  • Deterministic Workflows define fixed paths using the edges parameter.
  • Edges can be simple tuples or use dictionaries for conditional routing.
  • This approach is preferred for well-defined business processes where you want maximum predictability and lowest latency.
  • You can mix and match: a node in a deterministic Workflow can itself be a dynamic @node workflow!