The Agent Development Kit - ADK

Today we’re going to dive into the Agent Development Kit, better known as ADK. It’s a framework that is changing the game for building artificial intelligence agents.Think about this for a moment. For those who build software, the idea of creating a functional AI agent with a single line of code isn’t just a question—it’s the dream come true.Developers know this headache well. You start with a simple idea, but soon the project turns into a maze of API calls, state management, and repetitive code. Everything becomes chaos. And that’s exactly where ADK steps in to bring order.What’s interesting is that it brings solid software engineering principles—like modularity and testability—to AI development. It’s an elegant solution to a complex problem.🔩 The six key componentsTo understand how ADK works, you have to look under the hood. The system is made up of six fundamental building blocks, working like an orchestra where each instrument plays an essential role.The agent: It’s the brain of the system. It processes requests, makes decisions, and knows when to use its tools.The runner: It acts as the conductor. It ensures everything flows correctly: messages are delivered, tools are triggered, and responses return smoothly.The session: It’s the short-term memory. It keeps conversations coherent, remembers what’s being discussed, and maintains context.The state: It’s like a shared whiteboard. All components can read and write data there. A tool can leave information and the agent can use it instantly.The memory: It represents long-term knowledge. It stores information that persists across conversations, allowing the agent to learn and improve over time.The tools: They are the agent’s hands. Without them, it could only chat—but with them, it can act: search the web, connect to APIs, fetch data. They are what turns a chatbot into a truly useful assistant.🎼 How the symphony worksWhen a request comes in:The session loads conversation context.The agent processes the request.If necessary, a tool steps in.The state is updated with the results.The runner orchestrates everything to return a coherent response.A perfect loop that brings intelligent agents to life.🧰 Extend capabilities with toolsThere are two ways to give agents new skills:Build custom tools: Designed for specific business logic. They only require four elements:A unique nameA clear descriptionA schema with the required input dataThe function that performs the actionUse the Model Context Protocol (MCP): A universal connector that lets you integrate existing services (like GitHub, Discord, or Slack) without bespoke integration code.Additionally, ADK is model-agnostic: it works with OpenAI, Anthropic, or any model. Total freedom.⚙️ Workflows and collaboration between agentsWhen a task is too large for a single agent, workflows come into play, allowing you to orchestrate multiple agents to automate complex processes.It’s key to distinguish between:Multi-agent systems: designed for dynamic conversations (like a support bot that escalates to a specialist).Workflows: for pure automation, with fixed, predictable steps (for example, generating a monthly report).The most common patterns are:Sequential: One agent gathers data, another drafts, and another publishes. Ideal for step-by-step processes, like content creation.Parallel: Splits a task across multiple agents at once (for example, technical review and business review simultaneously). You gain speed and multiple perspectives.Loop: One agent generates a result, another evaluates whether it meets the criteria, and if not, it returns it with feedback to improve. Perfect for iterative refinement and continuous improvement.Rule of thumb:Step-by-step → sequentialSpeed or diversity → parallelMaximum quality → loop🔌 Connecting to the outside world: the universal adapter (MCP)The Model Context Protocol (MCP) acts like a universal power adapter. It connects agents to external services without the need for custom integrations.This separates the agent’s logic from the technical complexity of external connections.Real-world examples:A DevOps assistant that detects issues in GitHub and alerts via Discord.A support agent that queries the CRM and notifies the customer via Telegram.The automation potential is enormous.🔗 Referencias esencialesGoogle – Kit de Desarrollo de Agentes (ADK)This is the main resource. All official documentation can be found here.Doc oficial: Agent Development KitGitHub SDK: Agent Development Kit (ADK) Web🚀 ConclusionADK is much more than a set of tools. It represents a new philosophy for AI development, where agents are built with the same rigor as professional software.The final question:What will be the next big problem we automate