Multi-Agent Systems on GCP: Workflow Patterns with ADK and Terraform 🧠

ADK gives you four ways to orchestrate multi-agent systems - hierarchical delegation, sequential pipelines, parallel fan-out, and iterative loops. Here's how to build each pattern with Terraform provisioning the infrastructure. In the previous posts, we deployed a single Vertex AI agent with tools. That handles focused tasks well. But complex workflows need multiple agents: one to research, one to write, one to review. Or one to handle orders while another handles payments. ADK provides four orchestration primitives for building multi-agent systems. Unlike managed supervisor patterns, ADK gives you code-level control over how agents interact - sequential pipelines, parallel fan-out, iterative refinement loops, and LLM-driven delegation. Terraform provisions the infrastructure; Python defines the agent team. 🎯 🏗️ Four Multi-Agent Patterns Pattern Agent Type How It Works Best For Hierarchy LlmAgent with sub_agents Parent delegates to children based on LLM reasoning Dynamic routing, cus