标签：#adk

When building multi-agent systems, rigid state graphs quickly fall apart in the face of dynamic user inputs. Imagine building a smart assistant: a user hands you a checklist of three household chores today, but tomorrow it might be a list of ten software debugging tasks. Because the number of tasks, their sequence, and their execution details are entirely runtime-dependent, you cannot hardcode this path at design time. Forcing dynamic lists of work into a static graph-based workflow can lead to fragile, over-engineered code. You need a workflow that adapts dynamically at runtime. The Google Agent Development Kit (ADK) provides a flexible programming model to define dynamic workflows . With the release of ADK 2.4.0 , triggering these workflows has become even more seamless: you can register a Workflow directly in an agent's tools list, allowing the coordinator agent to execute it automatically as a first-class tool. In this article, you learn how to configure and trigger a dynamic workflow directly from a coordinator agent. This guide uses a task list coordination example, but you can adjust this pattern to other dynamic orchestration needs. The architecture of a dynamic workflow Static workflows define the execution path at design time. Dynamic workflows, however, allow agents to invoke tools, spawn other nodes, and schedule sub-agents conditionally at runtime. The system consists of three main components: Root agent ( root_agent ) : Gathers the list of tasks from the user, requests final approval, and directly calls the tasks_workflow tool. The workflow ( tasks_workflow ) : A Workflow that iterates over the approved tasks. Sub-agent ( task_explainer ) : An Agent tasked with generating a step-by-step execution plan for each task. Here is the architectural diagram of the solution: Technical implementation Let's break down how to implement this solution using the Google ADK library in Python. The complete code resides in the devrel-demos repository with core logic in

TL;DR — This article is going to be jam-packed with useful information, tips, tricks and hacks for setting up an agentic development in the Google ecosystem. This one isn’t really about the FinOps! Welcome to Part 2 Welcome back, friends! In the first part , I described the purpose of the FinSavant FinOps solution, the motivation for creating it, its overall architecture and tech stack, and how it works. In this part, we’ll use FinSavant as a case study in how to set up a development environment for the purposes of building such an ADK-based agentic solution. Even if you’re not particularly interested in FinSavant itself, I hope you’ll find a bunch of useful information and tips here that will help you build your own agentic solutions more effectively and quickly. We’ll cover: Using Antigravity IDE Overall project workspace structure Setting up agent skills for your coding agent My project’s GEMINI.md (or if you prefer, AGENTS.md ) My documentation approach Setting up MCP servers for your coding agent, such as BigQuery MCP Scaffolding the initial ADK agent using Google Agents CLI and its supporting skill Getting started with a Makefile Sound good? Let’s get cracking! Series Orientation Let’s see where we are in this series. Goals, Architecture, and Tech Stack: Capabilities, project goals, target architecture, technology stack, and design decisions. Development Environment Setup, Google Antigravity, MCPs and Skills, and ADK Bootstrapping with Agents CLI 📍 You are here. Building the ADK Agent and API Designing and Building the UI with Google Stitch and A2UI Deployment with Gemini Enterprise Agent Platform, Agent Runtime, Cloud Run and IAP Automating Deployment with CI/CD and Terraform Agent Observability, Evaluation, and Tuning with Gemini Enterprise Agent Platform Getting Started with Antigravity IDE These days, my favourite coding environment for any significant project is Antigravity IDE. This is Google’s agent-first integrated development environment. You get a lo

This week's Java roundup for July 6th, 2026, features news highlighting: the GA release of TornadoVM 5.0; point releases of JHipster, Keycloak and Google ADK; maintenance releases of GraalVM Native Build Tools and Micronaut; the OmniFish Build of Payara and introducing Vidocq, a new implementation of the Jakarta EE 11 Core Profile and MicroProfile 7.1. By Michael Redlich