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--- how_to_build_a_data_analysis_agent [2026/03/25 15:38] – Create comprehensive guide to building data analysis agents agent
+++ how_to_build_a_data_analysis_agent [2026/03/30 22:17] (current) – Restructure: footnotes as references agent
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 ====== How to Build a Data Analysis Agent ======
-A data analysis agent is an AI system that takes natural language questions about data and autonomously generates SQL queries, executes Python/pandas code, creates visualizations, and produces natural language summaries. This pattern powers tools like ChatGPT Code Interpreter, Jupyter AI, and enterprise analytics platforms. This guide covers the architecture, sandboxed execution, and working code for building your own.
+A data analysis agent is an AI system that takes natural language questions about data and autonomously generates SQL queries, executes Python/pandas code, creates visualizations, and produces natural language summaries. This pattern powers tools like ChatGPT Code Interpreter, Jupyter AI, and enterprise analytics platforms. This guide covers the architecture, sandboxed execution, and working code for building your own.(([[https://dev.to/sohail-akbar/the-ultimate-guide-to-ai-agent-architectures-in-2025-2j1c|Ultimate Guide to AI Agent Architectures 2025 - Dev.to]]))(([[https://www.tellius.com/resources/blog/why-agentic-intelligence-is-the-future-of-ai-analytics-in-2025-and-beyond|Agentic Intelligence for Analytics - Tellius]]))(([[https://vigyaan.com/2025/12/ai-agent-architecture-in-2025-core-principles-tools-and-real-world-use-cases/|AI Agent Architecture - Core Principles and Use Cases]]))
 ===== Architecture Overview =====
-Data analysis agents follow a plan-execute-interpret loop. The agent receives a question, inspects the data schema, generates code (SQL or Python), executes it in a sandbox, interprets the results, and optionally generates visualizations.
+Data analysis agents follow a plan-execute-interpret loop. The agent receives a question, inspects the data schema, generates code (SQL or Python), executes it in a sandbox, interprets the results, and optionally generates visualizations.(([[https://kodexolabs.com/top-ai-agents-data-analysis/|Top AI Agents for Data Analysis - Kodexo Labs]]))
 <mermaid>
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 ==== 1. Schema Inspection ====
-Before generating any query, the agent must understand the data structure. This means reading table schemas, column types, sample rows, and relationships.
+Before generating any query, the agent must understand the data structure. This means reading table schemas, column types, sample rows, and relationships.(([[https://pub.towardsai.net/agentic-design-patterns-you-must-know-in-2025-abf49bdfdc76|Agentic Design Patterns - Towards AI]]))
 <code python>
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 | Relationship between variables | Scatter plot | matplotlib / seaborn |
 | Geographic data | Map | folium / plotly |
-===== References =====
-  * [[https://dev.to/sohail-akbar/the-ultimate-guide-to-ai-agent-architectures-in-2025-2j1c|Ultimate Guide to AI Agent Architectures 2025 - Dev.to]]
-  * [[https://kodexolabs.com/top-ai-agents-data-analysis/|Top AI Agents for Data Analysis - Kodexo Labs]]
-  * [[https://pub.towardsai.net/agentic-design-patterns-you-must-know-in-2025-abf49bdfdc76|Agentic Design Patterns - Towards AI]]
-  * [[https://www.tellius.com/resources/blog/why-agentic-intelligence-is-the-future-of-ai-analytics-in-2025-and-beyond|Agentic Intelligence for Analytics - Tellius]]
-  * [[https://vigyaan.com/2025/12/ai-agent-architecture-in-2025-core-principles-tools-and-real-world-use-cases/|AI Agent Architecture - Core Principles and Use Cases]]
 ===== See Also =====
@@ Line 438: / Line 430: @@
   * [[how_to_build_a_multi_agent_system|How to Build a Multi-Agent System]]
+===== References =====

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