Building a Data Analyst Agent with Google ADK.
Lessons in Agentic Workflows At the recent Build with Google AI event in Kisumu, the core focus centered around a fundamental shift: moving from single-prompt chat completion to Agentic Workflows . Instead of asking one LLM to solve a complex problem in a single turn, agentic patterns split tasks across specialized, autonomous units coordinated by an orchestrator. To explore this hands-on, I…
At the recent Build with Google AI event in Kisumu, the emphasis shifted from single-prompt chat completion to Agentic Workflows. Rather than a single large language model handling a task, agentic patterns involve breaking down complex tasks into specialized, autonomous units coordinated by an orchestrator. To demonstrate this, I constructed a Data Analyst Agent using Google Agent Development Kit (ADK).
Google ADK is an open-source Python framework for constructing and testing AI agents. It provides agents with specific roles and instructions, tools like custom functions for autonomous use (SQL execution, Python scripts, APIs), and a dynamic routing system to chain multiple agents together. The Development UI & Tracing feature allows real-time monitoring of API calls and inspection of payloads to debug agent reasoning.
A crucial aspect of building agentic systems is the Model Context Protocol (MCP), a standardized bridge between AI models and external data sources or execution environments. MCP allows agents to securely retrieve context, access files, and invoke tools across various systems without hardcoded custom integrations for each database or API.
During local testing in the ADK web UI, I encountered two configuration issues. First, requesting a non-existent model resulted in a 404 NOT_FOUND error. The fix was updating the model configuration to use a valid Vertex AI endpoint, gemini-2.5-flash. Second, a malformed resource string caused a 400 INVALID_ARGUMENT error. The solution was removing a stray space in the .env file and agent.py.
After resolving the configuration issues, refreshing the local session with gcloud auth application-default login yielded a clean 200 OK status. This enabled the agent to successfully process data requests and generate summaries. The key lessons learned included the importance of correct configuration syntax, standardizing tools with MCP for seamless external connections, and utilizing local tracing to swiftly identify API-level bugs.
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