This article is an expanded write-up of the talk I recently delivered as a Google Developer Expert during a talk in Denver. The full slide deck is embedded below for easy reference. Why another “agent framework”? Large-language models (LLMs) are superb at generating prose, but production-grade systems need agents that can reason, plan, call tools, and respect enterprise guard-rails. Traditionally, that means: Hand-rolling connectors to databases & APIs Adding authentication, rate-limits, and connection pools Patching in tracing & metrics later Hoping your YAML jungle survives the next refactor Google’s new duo— MCP Toolbox and the Agent Development Kit (ADK) —eliminates that toil so you can treat agent development like ordinary software engineering. MCP Toolbox in one minute ⏳ What Why it matters Open-source MCP server Implements the emerging Model Context Protocol ; any compliant age...
In our previous overview, we introduced the Google Agent Development Kit (ADK) as a powerful Python framework for building sophisticated AI agents. Now, let's dive deeper into some of the specific features that make ADK a compelling choice for developers looking to create agents that can reason, plan, use tools, and interact effectively with the world. 1. The Core: Configuring the `LlmAgent` The heart of most ADK applications is the LlmAgent (aliased as Agent for convenience). This agent uses a Large Language Model (LLM) for its core reasoning and decision-making. Configuring it effectively is key: name (str): A unique identifier for your agent within the application. model (str | BaseLlm): Specify the LLM to use. You can provide a model name string (like 'gemini-1.5-flash') or an instance of a model class (e.g., Gemini() ). ADK resolves string names using its registry. instruction (str | Callable): This is crucial for guiding the agent's be...