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.
Google's Agent Development Kit (ADK) is an open-source, code-first Python framework for building and testing AI agents. It gives you:
adk web) to monitor API calls, inspect payloads, and debug agent reasoning in real time.
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.
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:
404 NOT_FOUND)
gemini-1.5-flash), causing the platform to reject the request. gemini-2.5-flash. 400 INVALID_ARGUMENT)
"gemini-2.5 flash" instead of a hyphen), breaking the API URL parser..env and agent.py.
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.