A new guide demonstrates advanced features of Google’s Agent Development Kit (ADK) by constructing a system of autonomous agents, each tasked with specialized functions. Through a modular design and integration with Google Search, the setup leverages the Gemini model to coordinate research, computation, data analysis, and written summaries within a unified architecture. The walkthrough reveals how ADK can power scalable, intelligent workflows suitable for enterprise applications.
The process begins with installing the google-adk package and importing core libraries to assemble the agent framework. Authentication relies on a Google API key loaded from an environment variable or entered securely at run time via Python’s getpass module. This approach safeguards sensitive credentials and grants each agent seamless access to Google’s suite of tools.
A custom TaskResult data class captures each agent’s output along with status information and metadata. Agents are then defined with clearly defined roles:
- The research agent taps into Google Search to gather up-to-date information.
- The calculation agent performs complex arithmetic and financial modeling.
- The analysis agent processes datasets to extract trends and generate insights.
- The writing agent transforms findings into polished executive summaries.
Workflows are executed asynchronously, making it possible to run multiple agents in parallel. Each agent’s run method returns a TaskResult object, allowing the main program to collect outcomes and log performance metrics. This design promotes efficient use of resources and clear tracing of each task’s progress.
The main() function initializes the entire multi-agent system and triggers the demonstration sequence. Compatibility is maintained across both standalone scripts and interactive notebooks, enabling developers to launch the process with await main() inside a Colab or Jupyter environment. The final output displays a concise summary of every agent’s work.
Testing showed the system handling a range of scenarios—real-time web queries, sophisticated numeric operations, data trend evaluation, and well-crafted textual reports. Built-in exception handling and modular hooks make it straightforward to extend or replace agents with additional tools. Developers can integrate new APIs or custom models without altering the core orchestration logic.
This hands-on example illustrates how ADK serves as a foundation for constructing robust, production-ready agent networks. Teams can adapt this blueprint to automate complex workflows, integrate enterprise services, and expand agent capabilities over time. Future efforts may explore advanced scheduling strategies, distributed deployments, and tighter integration with specialized APIs.