# Useful but Little-Known Features of ADK 2.0 🌍 What if you could design your AI agent workflows as explicit graphs, with full control over every execution path? ADK 2.0's Workflow class lets you build graph-based agent workflows by assembling nodes and defining execution paths through edges. Nodes can be AI agents, custom code functions, tools, or even nested Workflows, giving you precise control over complex branching structures. 📌 Title: Graph-Based Execution with the Workflow Class 🔗 URL: 🧩 Overview The Workflow class is the primary construct for building graph-based agent workflows in ADK 2.0. You define nodes (AI agents, functions, tools, or nested Workflows) and connect them with edges. Sequential execution uses tuple syntax like `("START", node1, node2, node3)`, while conditional branching uses dictionary-based routing. This explicit mapping of routing logic provides more reliable and maintainable orchestration compared to prompt-based control flow. 🛠 How to use it Create a Workflow instance with a list of nodes and edges defining their connections. ```python from adk import Workflow, Agent agent_a = Agent(name="researcher", ...) agent_b = Agent(name="writer", ...) workflow = Workflow( name="content_pipeline", nodes=[agent_a, agent_b], edges=[("START", agent_a, agent_b)] ) ``` For sequential flows, list nodes in a tuple. For conditional branching, use dictionary-based routing. You can nest Workflows as nodes to build hierarchical pipelines. 🏗 Building it into production ・Separate node responsibilities clearly so each can be tested independently ・Implement error handling at the node level to contain failure blast radius ・Use nested Workflows to create reusable sub-pipelines ・Document conditional routing logic to maintain team-wide clarity 💡 Use cases 📄 Build a collect → summarize → review → publish pipeline as a single graph 🔀 Route to different processing pipelines based on input data type 🏢 Orchestrate multi-department approval flows with agent nodes 🔁 Reuse sub-workflows across multiple automation projects ⚠️ Watch out The Workflow class is incompatible with Live Streaming and some third-party integrations. If you need real-time streaming responses, you'll need a different approach. Also, as graph complexity grows, debugging becomes harder — keep node granularity appropriate and document your edge definitions well. ✨ Explicit graph definitions dramatically improve the reliability and maintainability of agent workflows. If you're building complex processing flows, this is the feature to reach for. #ADK# #AIAgent#