Data analysis

A data analysis agent turns natural-language questions into real analysis: it writes and executes queries or Python against your data, inspects the results, and iterates until it can answer with numbers and charts instead of guesses. The critical distinction from plain chat is that the model never computes statistics in its head — it generates code, a code interpreter or SQL engine runs it, and the agent reasons over the actual output. LLMs are unreliable calculators but excellent analysts when handed an execution environment.

For anyone building AI-powered software, this pattern unlocks the “talk to your data” features every product team now wants: self-serve dashboards, anomaly explanations, automated reporting. It also demonstrates a principle you’ll reuse everywhere — delegate deterministic work to deterministic tools. The agent’s value is in choosing what to compute, interpreting results, and deciding the next question, not in doing arithmetic.

In practice, you’ll wire up a sandboxed interpreter tool (run_python or run_sql), feed the agent the schema — table names, column types, a few sample rows — rather than the raw data, and let the loop run: draft a query, execute, examine the result, refine. Guardrails matter here: enforce read-only credentials, cap query cost and row counts, and have the agent state its assumptions in the final answer. A well-built analysis agent shows its generated code alongside its conclusions, so users can verify rather than trust.