The Agentic Analytics Playbook
A practical guide to shipping analytics agents that last.
I spent the last few months studying how data teams actually build analytics agents that work. I spoke to 300+ of them and ran three meetups on it. This playbook is what I found, start to finish. How to scope an agent, choose your tools, build the context, roll it out, and measure it.
It is free.
Claire Gouze, CEO at nao
Get the playbook- A step-by-step roadmap from first POC to company-wide rollout, in five parts.
- A benchmark of 20+ analytics agent tools, scored on reliability, cost, speed, end-user UX, and data-team fit.
- The full context engineering method nao used to take an agent from 17% to 86% reliability.
- Real analytics agents from companies like nao, OpenAI, Anthropic, and Vercel, with their setup and results.
Five parts, in order.
Each one builds on the last, from scoping the agent to measuring it in production.
- 01
Scope
Start narrow, then expand.
Pick one user type, data team or business team, and one first use case. Decide what is in scope before the agent connects to anything.
- 02
Tools
Invest in the right tool for your team.
A benchmark of 20+ tools across warehouses, BI, general agents, and frameworks, with a build-or-buy decision tree to place your own setup.
- 03
Context
Your agent is as good as its context.
What context engineering is, the four things an optimal context balances (answer rate, accuracy, cost, speed), and a five-step method to get there.
- 04
Roll-out
Expand company-wide with trust.
A five-phase model from first test to steady state, with who gets access at each step and how you know you are ready for the next one.
- 05
Results
Measure what matters.
The four metrics that tell you two things. Whether the agent is trustworthy, and whether people actually use it.
It ends with five real-world examples and a one-page cheat sheet that fits the whole thing on a single page.
Read the playbook.
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