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 built a Data Analyst Agent using the Google Agent Development Kit (ADK). Here's a quick look at the build, the bugs I bumped into, and the concepts behind them. What is Google ADK? Google's Agent Development Kit (ADK) is an open-source, code-first Python framework for building and testing AI agents. It gives you: - Agents: Individual units with specific roles and instructions. - Tools: Custom functions (SQL execution, Python scripts, APIs) that agents invoke autonomously. - Orchestration: Dynamic routing loops to chain multiple agents together. - Development UI & Tracing: A local server ( adk web ) to monitor API calls, inspect payloads, and debug agent reasoning in real time. Crucial Concept: Model Context Protocol (MCP) A key concept when building agentic systems is the Model Context Protocol (MCP). MCP serves as a standardized bridge between AI models and external data sources or execution environments. Rather than hardcoding custom integrations for every database or API, MCP gives agents a uniform interface to securely read context, access files, and call tools across different systems. The Build & How I Fixed the Roadblocks I instantiated the agent in agent.py using standard ADK imports: from google.adk import Agent data_agent = Agent( name="data_analyst", model="gemini-2.5-flash", instruction="You are an expert Data Analyst AI...", ) During local testing in the ADK web UI, I hit two quick configuration bumps: 1. Requesting a Non-Existent Model (404 NOT_FOUND ) - The Issue: The agent attempted to contact a model string that didn't map to a valid Vertex AI endpoint ( gemini-1.5-flash ), causing the platform to reject the request. - The Fix: I updated the model configuration to a valid target identifier: gemini-2.5-flash . 2. A Malformed Resource String (400 INVALID_ARGUMENT ) - The Issue: A formatting slip left a space in the string ( "gemini-2.5 flash" instead of a hyphen), breaking the API URL parser. - The Fix: I removed the stray space across .env andagent.py . Finalizing the Credentials After fixing the config files, I refreshed my local session using gcloud auth application-default login . Re-running adk web gave a clean 200 OK status, allowing the agent to successfully process data requests and generate summaries. Key Takeaways - Watch Configuration Syntax: Model names must exactly match active provider endpoints; minor typos break API routing. - Standardize Tools with MCP: Leveraging protocols like MCP makes connecting agents to external databases and environments seamless. - Use Local Tracing: Running local inspection interfaces ( adk web ) drastically speeds up finding API-level bugs. Top comments (0)