How do you implement AI agents for data analysis and business intelligence?

Data analysis agents enable natural language interaction with business data. Architecture: the agent receives a natural language question, determines the analytical approach (SQL query, statistical analysis, visualization), executes the analysis using tools, and presents results with insights. Tool suite: SQL executor (query databases with generated SQL), Python executor (run pandas, numpy, scipy for statistical analysis), visualization generator (create charts using matplotlib, plotly, or chart specifications), and report formatter (compile findings into structured reports). Implementation: provide the agent with database schema descriptions, statistical method knowledge, and visualization best practices. The agent plans the analysis (determine which data is needed, what calculations to perform, how to present results), executes step by step (query data, transform, analyze, visualize), and synthesizes findings into plain language insights. Safety: execute all code in sandboxed environments, implement query timeouts and resource limits, restrict database access to read-only, and validate generated SQL before execution. Advanced capabilities: anomaly detection (identify unusual patterns in data), trend analysis (detect and explain trends over time), comparative analysis (compare metrics across segments, time periods, or cohorts), and predictive analysis (forecast future values using time-series models). Report generation: auto-generate executive summaries from analytical findings, include visualizations with annotations, provide data source attribution and methodology notes, and format for various outputs (email, PDF, dashboard). Evaluation: measure query accuracy (does the SQL return correct data), insight quality (are the interpretations accurate and valuable), and usability (do users find the output actionable).