How do you implement an AI agent for data analysis and reporting?

A data analysis agent combines natural language understanding with code generation and execution. Architecture: parse the user's analytical question, generate SQL or Python code to answer it, execute in a sandboxed environment, interpret results, and present findings with visualizations. Implementation: the agent has tools for SQL query execution (read-only against the data warehouse), Python code execution (in a sandboxed container with pandas, matplotlib, seaborn), and report generation (markdown with embedded charts). Workflow: user asks "What were our top 10 products by revenue growth last quarter?" - agent generates SQL to calculate growth rates, executes it, generates a bar chart using matplotlib, and presents results with narrative analysis. Safety: execute code in isolated containers with no network access, set resource limits (CPU, memory, execution time), restrict file system access, and validate SQL against an allow-list of operations (SELECT only). Advanced capabilities: iterative analysis (agent notices interesting patterns and investigates further), statistical testing (agent applies appropriate tests based on data characteristics), and automated insights (proactively flag anomalies, trends, and correlations). Integration: connect to data warehouses (Snowflake, BigQuery, Redshift), BI tools (Tableau, Metabase), and notification systems for scheduled reports.