Definition

Agentic analytics

Agentic analytics means an AI analyst runs the full loop of an analysis, from framing the question to querying, checking and writing it up, while people own the definitions and the decision.

Chatting with a model about your data is one thing. You paste a table, ask a question, get a paragraph. Agentic analytics is a system built around the model. It connects to the warehouse and reads the metric definitions you wrote. It plans the analysis, runs the SQL, makes the chart, and drafts the memo. Then it publishes where your team already reads. It also checks itself along the way, if you built the checks.

What you get depends on the system around the model more than on the model. The same model gives a different quality of answer depending on what sits around it. The skills that encode how your team analyzes. The context store with your definitions. The tools it can call. The eval suite that tells you when it is wrong. Swap the model and most of that stays. That is why the course spends one week building the analyst and four weeks on everything around it.

A concrete example. A VP writes "checkout conversion dropped, can you look into it?" A chat model asks what data you have. An agentic analyst pulls the funnel from the warehouse and decomposes the drop by segment and step. It checks the segment sizes, produces the chart, and writes a memo with the query behind each number. You read the memo and decide. You still make the call. The pulling and checking moved to the system.

The failure modes are different too. An agentic system rarely crashes. It returns a confident number computed on the wrong definition, or on the wrong date field. Or on a sample that was filtered without telling you. Shane's post on ten ways an AI analyst breaks lists the ones we keep hitting in real companies.

In the courses

Agentic Analytics: Build an AI Analyst is five weeks on this. Week 1 (Foundations of agentic analytics) builds the first skill and a small agent system. Week 2 (Design the system, connect MCPs and a warehouse) connects it to data and to your team's tools. Weeks 3 to 5 measure it, give it context, and swap the engine. AI Analytics for Everyone is the other side. You use these systems rather than build them, and you check the answers yourself.

Read more

Posts and recorded workshops on this site that cover the term.

Posts
NOV 10, 2026
The Weekly Query: How do you build and maintain the context your AI analyst needs to scale to the rest of the company?
Company context, domain guides, semantic models, open data jobs and Saturday’s $25 Hex workshop.
OCT 27, 2026
The Weekly Query: Why is AI analytics harder than AI coding?
What's inside: building an AI analyst you can trust, AI analytics headlines, capability and reliability, eight data jobs, and upcoming workshops.
FEB 19, 2026
The AI Analyst Genome: Designing a Multi-Agent System That Rebuilds Itself From a Single File
How we designed a single markdown file that regrows an entire multi-agent AI data analyst from scratch. Architect debates, builder agents, and the blurry line between prompts and specifications.
OCT 20, 2026
The Weekly Query: What should data teams actually learn about AI right now?
What's inside: AI skills for data teams, Claude Dashboards, context management, eight data jobs, and free workshops.
FEB 21, 2026
The Correction System: What Happens When You Validate Every Number Your AI Analyst Produces
I manually validated every number my AI analyst produced against my own SQL. Found out I was the one who had it wrong. Then I started noticing what the correction log was turning into.
SEP 20, 2026
ChatGPT's Data Agent: Senior Technical Execution, Junior Business Judgment
We ran 70 tests on OpenAI's data agent. It caught almost every statistical trap and failed the same organizational errors people do. Where I would use it and where I would not.

Next cohorts start Oct 19 and Nov 2.

AI Analytics for Everyone
$1,800 · Oct 19 · ★ 4.9/5
Enroll on Maven
Agentic Analytics: Build an AI Analyst
$2,500 · Nov 2 · ★ 4.9/5
Enroll on Maven
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