Agentic Analytics

Fair warning: this article goes a little bit back to the roots.

Back in 2022, I wrote my first book, AI-Powered Business Intelligence, about how AI could make analytics faster, easier, and overall more capable.

A few months later, ChatGPT gave us the viral version of what I previously described as “using natural language to get faster insights from your data.”

Four years later, we’re not just chatting with smarter models. Today’s AI can run entire analytical workflows all by itself – that’s Agentic Analytics.

The question is whether you should let it do that – and what needs to be in place for you to actually take advantage of it.

Let’s find out.

Imagine you work with a software delivery team and want to predict the next release date.

You export several months of data from Jira, including issues, their current status, sprint dates, and project descriptions.

You upload the files to ChatGPT or Claude and ask:

“Will our next major release land within the next 5 weeks?”

Within minutes, the AI inspects the files, identifies the remaining work, calculates the team’s velocity, and estimates the remaining timeline – perhaps even with a nice visualization:

Holy smoke, that’s genuinely impressive!

But as with everything that seems to be “too good to be true”, there’s a downside you need to understand.

Let’s say the AI concludes:

“No – the release probably won’t finish”.

That answer might be correct.

Or it could be completely wrong.

The result depends on definitions and assumptions the AI may have chosen without asking you:

For example:

For a quick ballpark estimate, these “details” might not matter so much.

But if you need to make a 6-figure budget decision based on this estimate, you want to know exactly what you’re dealing with.

That’s why professional analytics needs more than a plausible conclusion. It needs a transparent and reproducible process that tells you the exact conditions under which the answer is valid.

Chat with your data works best when your data “wants to be chatted with”: highly curated, governed, documented, relatively unambiguous, and easy to verify.

In theory, you could specify all of that inside the chat. (We kind of tried it two years ago.)

But then you’re essentially rebuilding an analytical system inside a chat interface.

It can work, but let me put it like this: You’ll hit a ceiling pretty fast.

Agentic Analytics gives AI a working environment for performing analytics.

The chat remains as a communication channel. But it is no longer the place where the analysis itself lives.

Instead of asking AI to produce an answer however it chooses, you give it a governed analytical workflow. The chat merely triggers that workflow.

For our release forecast, the agent might need to:

The agent can adapt its approach to the situation.

But it should only adapt within defined boundaries.

If it can silently redefine business terms, use unapproved tools, or skip quality gates, the analysis becomes much less useful.

Reliable Agentic Analytics still depends on solid data foundations, including data design patterns that have been established over the last 30 years.

But data foundations alone are not enough. Three additional components are especially important:

Together, these components form what I call an analytical contract – the boundaries within which the agent can adapt without improvising the rules.

It doesn’t guarantee one universal truth. Conclusions still depend on the data, assumptions, and context.

But it makes the process inspectable and repeatable: you can see how an answer was produced and rerun the same governed process under defined conditions.

Anthropic recently described a similar architecture for their internal analytics system.

They report automating 95% of their business analytics queries through Claude. Of course, these are self-reported results and shouldn’t be treated as benchmarks. But it seems that adding Skills alone made a huge impact:

So the architecture points to the larger truth: Giving an AI access to your warehouse is not enough. It’s just the starting point.

I believe the main users of Agentic Analytics will be data analysts, data scientists, analytics engineers, and data teams more generally.

Not every business employee.

Most business users will continue interacting with data through spreadsheets, dashboards, and, increasingly, chat interfaces.

Which is totally fine.

What changes is the machinery behind those interfaces.

Today, data professionals still assemble many analytical workflows by hand.

“Can you tell me which campaign performed best last month and give me the data?” regularly leads into endless business-asks-data-team-tries-to-answer rabbit holes.

Which reporting period exactly? What do you mean by “best”? Which campaigns are comparable? Do obvious bot clicks count?

Calculating metrics is easy. Landing on metric definitions is the real work.

Agentic Analytics allows teams to encode more of that process once – with the help of AI – and reuse it repeatedly, at scale, without ending up in a backlog of unmaintainable business definitions.

Chat interfaces broaden access to analytics.

Agentic Analytics increases the production capacity of analytics teams.

We are still figuring that out.

My current view is that most near-term use cases will involve governed execution: a human starts the workflow, the agent performs the approved process, and a human reviews the result.

Narrow and thoroughly tested workflows may eventually run autonomously and escalate exceptions.

But the goal is not to replace in-house analysts, turn every employee into an analyst, or let unrestricted agents loose on the data warehouse.

The immediate opportunity is to help data professionals perform recurring analytical work at a much greater scale.

Don’t start by funding a general-purpose “chat with all our data” initiative or building a semantic layer for the entire organization.

Instead, fund one governed analytical workflow built around an explicit analytical contract and tied to one useful outcome.

Choose something valuable, recurring, and currently assembled manually. Campaign performance reporting could be one example; risk monitoring another.

The exact question, reporting period, or slice of data may change. But the underlying data sources, business definitions, approved methods, and validation checks should not have to be rebuilt from scratch every time.

The opportunity is to industrialize recurring analytical work without industrializing the conclusions.

That means more questions handled, faster turnaround, and greater confidence in insights generated with AI support – without lowering your analytical standards.

If you’d like to see how this works hands-on – I’m showing an end-to-end example in my live O’Reilly training, Agentic Analytics with Claude Code. (You can join for free with a trial.)

See you next Saturday,
Tobias

P.S. If you’re a business professional trying to wrap your head around “chat with your data”, my ChatGPT for Data Analytics Bootcamp is a better starting point.