General-Purpose Agents Are Mature. Do Vertical Apps Still Need Their Own?
How I let external Agents keep their general capabilities, use a built-in Agent for a deeper analytics workflow, and connect both to one control plane.
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Category
AI Analysis Insights
10 articles
AI Made Analysis Faster. Now It Is Harder to Explain Where a Conclusion Came From
Once AI Agents find data, write SQL, run notebooks, and revise reports, chat history and file versions no longer preserve the full evidence chain. This is why data analysis needs Analysis Lineage.
Read article →The Limits of Skills in Data Analytics: Why Skills Are Not Enough for Real-World Data Analysis
A Skill can preserve an analytical method and package SQL or context from a specific case. Real analysis also depends on data versions, execution state, human decisions, evidence, and limitations.
Read article →What Is the Difference Between an AI Agent and a Chatbot? From Answering Questions to Completing Verifiable Work
An AI agent does more than produce longer answers: it uses tools based on state, advances multi-step work, and leaves verifiable deliverables. This article explains the differences, evaluation criteria, use cases, and adoption boundaries through data analysis.
Read article →The Greatest Risk in AI Data Analysis Is Not a Calculation Error—It Is Not Knowing How the Number Was Calculated
The hardest AI analysis errors to detect come from definitions, denominators, joins, and time windows. This article presents four review layers: Plan, SQL, checkpoints, and limitations.
Read article →If Natural Language Can Query Data, Why Do We Still Need SQL in 2026?
Natural language lowers the barrier to querying data, but it cannot replace SQL as reviewable evidence for grain, joins, filters, and aggregation. The better division of work is AI drafting, SQL preserving evidence, and people reviewing it.
Read article →Why Should Formal Analysis Start with a Plan Before AI Executes?
A Plan aligns the decision question, denominator, grain, time window, and validation approach before data retrieval. Execute then becomes a clear human authorization boundary, preventing ambiguous assumptions from turning into official numbers.
Read article →What Is Reproducible AI Analysis?
Learn how reproducible AI analysis preserves inputs, SQL, assumptions, validation, and reports so teams can rerun results and explain every change.
Read article →AI Analysis Tools, Conversational BI, and Traditional BI: How Should They Divide the Work?
Analysis agents, conversational BI, and traditional BI serve different stages. Quick answers, definition exploration, and stable monitoring should not be confused. This article provides a five-dimensional selection matrix and three practical scenarios.
Read article →What Is Local-First AI Data Analysis?
Learn how local-first AI data analysis reduces data movement, queries local files, and preserves reusable work while clarifying cloud model boundaries.
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