Introducing Agent Development Kit (ADK): Your AI building blocks

- [Instructor] So we've planned for WanderWise to check the weather, build an itinerary, and get a smart packing list, all handled by intelligent agents working together behind the scenes. But how do you build such a sophisticated system? Perhaps Google's agent development kit, or ADK, can help. This open source framework is designed to power the next generation of multi-agent AI systems. As AI evolves beyond single purpose bots, developers face new hurdles, effectively coordinating multiple intelligent agents, ensuring system reliability at scale, and seamlessly integrating these agents with real world data and existing systems. ADK was specifically created to meet these challenges head on, providing a comprehensive toolkit to design, build, test, and deploy powerful multi-agent applications. At the very heart of ADK lies its modular architecture. This isn't about one size fits all. It's about providing the right building blocks. With ADK, you can construct LLM agents that leverage the power of large language models for sophisticated adaptive reasoning and dynamic task routing, workflow agents that are perfect for orchestrating tasks in predictable patterns, think sequential steps, parallel processing, or iterative loops, and custom agents, which give you the flexibility to tailor agents with unique business logic specific to your application's needs. Critically, these agents can be composed hierarchically. Imagine a complex system where a manager agent delegates tasks to specialized worker agents. This enables intricate delegation and precise coordination. For WanderWise, this means the main agent could orchestrate a dedicated weather agent, a packing agent, and an itinerary agent, each an expert in its domain all working together in a flexible ADK managed workflow. And how do these agents work together? ADK lets you define agent workflows with remarkable flexibility. You can use predictable patterns where you define interactions that are sequential, run in parallel, or loop based on conditions, or you can leverage LLM driven dynamic routing. This allows agents to adapt their behavior and choose the next step based on real-time context and LLM'S reasoning capabilities, making your system truly intelligent and responsive. Keep in mind, agents are only as good as the tools they can use. ADK excels here by allowing agents to utilize prebuilt tools, custom functions, or popular third party libraries and frameworks like Link Chain or Crew AI, integrating their capabilities directly into your ADK agents. What's more, ADK allows you to treat other agents as tools. This opens up possibilities for sophisticated graph-based orchestration and advanced delegation patterns where agents can call upon each other's specialized skills. While ADK is highly optimized for Google Cloud, Vortex AI, and advanced models like Gemini, it's fundamentally designed to be model agnostic and deployment agnostic. This is a massive advantage. Through integrations like Light LLM, you can use over 200 different models from providers like Anthropic, Meta, Mystral, and many others. ADK isn't just powerful, it's designed to make developers productive and help you build production ready systems. Key features include robust support with software development kits for both Python and Java, tools to evaluate not just the final outputs of your agents, but also their intermediate steps, ensuring quality and reliability, support for handling various data types like audio, video, and text in a streaming fashion, crucial for advanced interactive agent experiences and more. So to return to our WanderWise app and any complex multi-agent system you envision, Google's ADK provides that crucial, production-ready, open source foundation. It's the toolkit that lets you move from a brilliant idea to a deployed reality, enabling you to design, orchestrate, and scale advanced multi-agent AI systems quickly, reliably, and with confidence. The future of AI is collaborative, and with ADK, you have the power to build it.