Graph Workflows in ADK: Everything You Need to Know
Annie Wang
Google AI Cloud Developer Advocate
Shangjie Chen
Software Engineer, Google Cloud AI
Graph engineering is the design work: breaking a task into nodes, connecting them with edges, and deciding where code, models, or people control the next step. The Agent Development Kit (ADK)'s Workflow turns that design into an executable process, with functions and agents doing the work. Through a refund example, this post shows how to run steps in parallel, route decisions, pause for human review, and process a list of cases. It also explains when to declare the paths in a static graph and when to let Python schedule further work as results arrive.
TL;DR: Using a refund workflow in ADK, we'll cover fan-out and fan-in, deterministic and agent routers, human-in-the-loop pauses, parallel workers, and dynamic orchestration— along with when to use a static graph or let Python decide what runs next.
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