Building Multi-Agent Data Analysis Pipelines with Google ADK

Google ADK Multi-Agent Pipeline Tutorial: Data Loading, Statistical Testing, Visualization, and Report Generation in Python

Google ADK enables the construction of multi-agent pipelines for automated data analysis and reporting. The system coordinates five specialized agents—loader, statistician, visualizer, transformer, and reporter—to manage end-to-end workflows. This approach allows for structured, scalable data exploration using high-level LLM orchestration.

Why This Matters

Transitioning from manual notebook-based analysis to agentic pipelines addresses the challenge of state management and tool complexity in data science. By using specialized agents for tasks like hypothesis testing and visualization, engineers can build resilient systems that provide consistent, interpretable insights without the overhead of manual script maintenance. This architecture ensures that complex analytical libraries like SciPy and Matplotlib are used correctly and consistently across the entire data lifecycle.

Key Insights

• Google ADK utilized InMemorySessionService to track state across agent interactions in the 2026 tutorial.
• The statistician agent uses scipy.stats for Shapiro-Wilk and ANOVA tests to validate data distributions.
• Dynamic data transformation is performed using the transformer agent’s df.query and df.eval tools to prevent manual coding errors.
• Persistence is maintained through a centralized DataStore class that tracks dataset shapes and column metadata throughout the session.
• Automated visualization agents create comprehensive 4-plot distribution reports including KDE and Q-Q plots to identify skewness and normality.

Working Examples

Defining the Master Analyst agent that coordinates specialist agents in the Google ADK pipeline.

from google.adk.agents import Agent; from google.adk.models.lite_llm import LiteLlm; master_analyst = Agent(name='data_analyst', model=LiteLlm(model='openai/gpt-4o-mini'), description='Master Data Analyst orchestrating end-to-end data analysis', instruction='Load data -> Describe -> Visualize -> Analyze -> Transform -> Report', sub_agents=[data_loader_agent, stats_agent, viz_agent, transform_agent, report_agent])

Practical Applications

• Automated Financial Reporting: A master agent coordinates data loading, currency transformation, and outlier detection. Pitfall: Overwriting raw data; corrected by enforcing new dataset names for every transformation step.
• Customer Churn Analysis: A statistical agent runs Chi-Square tests on categorical features while the visualizer generates churn heatmaps. Pitfall: Misinterpreting p-values; corrected by agent instructions that require plain-language interpretation alongside statistics.

References:

• https://www.marktechpost.com/2026/04/13/google-adk-multi-agent-pipeline-tutorial-data-loading-statistical-testing-visualization-and-report-generation-in-python/