ADK 2.0 introduces a graph‑based Workflow Runtime that separates routing from LLM inference, replacing Sequential/Parallel/Loop agents with deterministic nodes, cutting token usage by ~50% and latency by ~20%, while adding HITL, automatic retries, and a clear migration path for Python and Go users.
The article explains how Google Antigravity's production‑grade agent engine, managed Agent API, SDK, and ADK let developers replace heavyweight infrastructure with a single API call or a lightweight library, and provides detailed criteria and code examples for choosing and using each option.
This guide walks through installing ADK-Go, configuring a simple LLM agent, handling model selection and errors, leveraging Google Search and custom function tools, and orchestrating complex workflows with SequentialAgent, LoopAgent, and ParallelAgent, all illustrated with concrete Go code examples and output.
The article explains ADK's three core orchestration modes—SequentialAgent for ordered pipelines, ParallelAgent for independent concurrent tasks, and LoopAgent for iterative quality‑control loops—detailing their suitable scenarios, state‑flow mechanisms, and how to build a complete order‑to‑delivery workflow without writing explicit orchestration code.
The article outlines the core building blocks of AI agents—including frontend frameworks, development kits, tool integration, memory strategies, design patterns, model selection, and runtime environments—while explaining how each choice impacts performance, scalability, cost, and security.
Google’s Agent Development Kit (ADK) has added Go language support, enabling developers to build modular, high‑concurrency AI agents with code‑first workflows, integrated debugging UI, A2A protocol for agent collaboration, and a suite of pre‑built tools for tasks like Gemini search and Cloud API calls.
This tutorial walks through why Multi‑Agent Systems, Anthropic's MCP and Google's A2A protocol are crucial for Agentic AI, then demonstrates how to set up two MCP servers, create a hierarchical trio of agents, enable inter‑agent coordination via A2A, and run the complete pipeline with detailed Python code and deployment tips.
